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[music playing].Please welcome the CEO.of Amazon Web Services, Andy Jassy..[applause].Thank you, and welcome.to the ninth annual AWS.re:Invent and the first virtual one..Now, I think we all wish we were.together in Las Vegas right now.but we’ve got the next best thing,.we’re live,.so I’m really excited.to be with you..I am going to share a few words.about re:Invent,.it’s a little bit.different this year..So one thing that’s similar is.we have a lot of people congregated..We have well over 400,000 people.who have already registered.for re:Invent..We will be at 500,000.by the end of the week,.and this year is going to be.different than prior years..re:Invent is going to be over.three weeks instead of one week,.and then we have five keynotes,.so, the one you have today..Thursday there will be.a partner keynote..Next Tuesday will be our first ever.machine learning keynote with Swami..Then, next Thursday,.you will have Peter DeSantis.telling you more about.our infrastructure and then,.Werner will do the anchor keynote.the third week..So we’ve got a lot.in store for you..re:Invent is first and foremost.an education.and learning conference.and this year will be no different..We have well over.500 technical sessions.and while I think we would.all like to be together,.I think one nice part.about it being virtual.is that you don’t have that session.collision that you sometimes have,.where there are two sessions.you want to go to at the same time.or you have to travel..They will all be online..Some will be scheduled..Most of them will be on demand..So it will be much easier for you.to see everything you want..A lot for you..So I am going to give a quick update..But before I do.I just want to acknowledge.what an unbelievably crazy last year.this has been,.particularly these last nine months..COVID has been so difficult.for so many people..We have had so many people pass away..We’ve had so many people.lose their jobs..So many businesses are struggling..It’s just been really difficult.and I think if you live.in the United States.it’s also pretty hard.to not be struck.by the murders of Ahmaud Arbery,.Breonna Taylor and George Floyd.and realize the sobering thought.that we have so far.to go in this country.in how we treat black people..I think the reality is for.the last several hundred years.the way we treated black people.in this country is disgraceful.and something that has to change..We’re working on it at Amazon..I know a lot of companies are.but I think the important thing.for us all to realize.is that this does not.get solved in a few months..It can’t be something that.we work on for a few months.and then turn.our attention elsewhere..It’s going to take several years.of us working together.but we need to do it..So a quick AWS update..The business is at a $46 billion.revenue run rate business.in the last announcement that we made.growing 29% year-over-year..I think it’s also important to talk.about that year-over-year right.because I think a lot of people.sometimes get confused.and they try to compare different.companies’ year-over-year growth rate..The year-over-year.growth rate percentage.only matters as it relates.to the base revenue..So you can have a higher.year-over-year growth rate.but be growing at.a much lower absolute rate.if you have a much lower.base of revenue..If you look at just AWS as an example.to grow to a $46 billion dollar.revenue run rate with 29%.year-over-year growth,.it meant we had to grow.an incremental $10 billion.in the last 12 months to get there..That is much larger than you.will see elsewhere in the cloud..I think another data point to give.you an idea of how fast AWS is growing,.it took us a 123 months,.a little over 10 years.to grow to a $10 billion business..Then it took us only 13 months.to go from $10 to $20 – er, 23 months -.to go from $10 to $20 billion,.13 months to go from $20 to $30 billion.and then 12 months.to go from $30 to $40 billion..So the rate of growth in AWS.continues to accelerate..I thought this was also.an interesting slide.which is: This shows.you the top enterprise.IT companies out there.ten years ago based on revenue..And you can see on this list.there’s some companies that don’t.really exist anymore in this forum.but you will also see.that AWS is nowhere.to be seen in this list..And then if you fast forward.just ten years in 2020.you can see that AWS is now.the fifth largest enterprise.IT company in the world..Ahead of companies like SAP.and Oracle and of course,.that growth is significantly driven.by the growth of cloud computing.and the infrastructure technology space..And I think that you can see that.with what’s happened.during the pandemic..And in the short term.in the first nine months.or ten months of this thing.virtually every company in the world,.including Amazon, has tried.to save money any way that they can..But what we have seen.and this happens a lot of times.when you have a period.of discontinuity like a pandemic.is that companies take a step back.and they rethink what they are doing.and what they want to stop doing..And one of the things that we’ve seen.is that enterprises.that we’ve been talking with.for many years.about moving to the cloud.where there’s a lot of discussion.and dipping the toes in the water,.but not real movement,.so many of those enterprises.have gone.from talking to having a real plan..And that I think is going to be.one of the biggest changes you'll see..See, I think when you look back.on the history of the cloud.it will turn out.that the pandemic accelerated.cloud adoption by several years..It's really hard to build a business.that sustains.for a long period.of time - really, really hard..And this is a metric.that I think is interesting..If you look at the Fortune 500.just 50 years ago in 1970.you can see that only 83 companies.or 17% of them.are still in the Fortune 500..If you look at just 20 years ago.the 2000 Fortune 500,.only half of them.are still in that list..It is really hard to build a business.that lasts successfully.for many years.and to do it you’re going.to have to reinvent yourself.and often you are going to have to.reinvent yourself multiple times over..And so, in the last nine months.I’ve thought a lot about reinvention.and what it takes.to do reinvention well..And typically what you see is.the desperate kind of reinvention..You see companies that are.on the verge of falling apart.or going bankrupt deciding.they have to reinvent themselves..And when you wait to that point.it’s a crapshoot.whether you’re going.to be successful or not..It’s a little bit.like borrowing money..Everyone will tell you that you don’t.want to have to be borrowing money.when the business is in bad shape.because you may not get the rates.you want.or you may not get money at all..You want to be reinventing.when you’re healthy..You want to be reinventing.all the time..And so we thought about.what are the keys to reinvention.and some of it is building.the right reinvention culture.and some of it is knowing.what technology is available to you.and jumping on it.to make that reinvention happen..So I thought I would share with you.today.what we see as some of the keys.to building that reinvention culture.and then some of the things that we.see being reinvented as we speak..So what does it take to reinvent?.And I am going to list eight keys.that I think are important.if you want to build.the right reinvention culture..And the first is that.you have to have the leadership will.to invent and reinvent,.and those terms sometimes.they are a little different,.they are also similar..If you think about it,.people often say invention.is inventing a new product or service.from whole cloth.and reinvention.is reimagining an existing concept..But if you’re going to reinvent.and reimagine.there’s a load.of invention in there..Just look at what Airbnb has done.in the hospitality space.or what Peloton has done.in the exercise bicycle space.or look at what Stripe has done.in the payment space..These are huge amounts of invention.that has gone into.reimagining these spaces..And so if you’re going to be a leader.that’s going to reinvent.you have got to be maniacal.and relentless and tenacious.about getting to the truth..You have to know what competitors.are doing in your space..You have to know what your customers.think about your product.and where you sit.relatively speaking..You have to know what’s working.and what’s not working..And you will always have a lot.of people inside the company.who will try and obfuscate.that data from you..Sometimes they think.they are doing you a favor.and sometimes it’s for.self-preservation reasons.but it’s hard to get at that data.and you have to be relentless about it..You have to challenge people..Often people who know a lot more.about a subject than you do.but you have got to get to the truth..And then when you realize that.there’s something you have.to reinvent and change.you have to have the courage.to pick the company up.and force them to change and move..And part of that is sometimes.acknowledging.that you can’t fight gravity..If you step back and have conviction.that something is going to change.because it’s a better experience.for customers, it is going to change..Whether you want it to or not,.whether it’s convenient for you or not,.it is going to change.and there are a lot.of examples of this..I think if you look at what.Reed Hastings and Netflix.did several years ago.where they cannibalized.their own DVD rental business.because they saw where it was.headed with streaming,.I think that turned out to be.a pretty good decision for them..I think if you look at Amazon.in the late 90s.we had this owned inventory.retail business.which meant we bought all this.product from publishers.and from distributors..We stored them in our warehouses.and then we shipped them.to customers..And what we started seeing was.these companies like eBay.and Half.com that were actually.offering third-party sellers’ products.and they were shipping.the products to customers..And we had this huge animated.debate inside the company.on whether or not.we should support that..And the reasons that.we were concerned about it.were we just didn’t believe.anybody was going to take care of.customers the same way that we did..And then also.the whole culture was set up.to be this owned inventory business..People worried, well if we worked.with third-party sellers,.how would our publishers.or distributors feel?.So it was a very hard decision.but ultimately, we decided.to build a marketplace.and offer third-party sellers..And we did it because we know.that you cannot fight gravity..It was better for customers..It provided them better selection.and it gave them more assortment.on price..Now that also turned out.to be a good decision for us.because we sell more.than a half of our retail products.through third-party sellers..But you have got to realize that.if something’s going to happen.it is going to happen regardless.of whether you want it to or not..You’re much better off.cannibalizing yourself.than having someone.do it to you and chasing it..The third thing you have got.to make sure of.is that you have talent.that’s hungry to invent..Now this seems fairly obvious..Everybody says I have talent.that wants to invent..But it’s not always true..A lot of people who have been.at the company.for a long time are very comfortable.doing things.the way they have been.doing them for a long time..Have you ever noticed it’s often when.you have new blood in the company.that they are leading.the transformation?.And that’s not because existing.people can’t lead reinvention..It's just that you’re asking them.to reinvent something they built..It’s hard to rip up something.you spent a lot of time.and energy and dedication doing..And it means you have got.to learn new skills.and it means that you have got.to actually be curious.about getting trained.on other technologies..Sometimes that’s true.sometimes that not true….I will tell you a quick story..There’s a CIO in a pharma life.sciences company.that I’ve known a long time..I have a huge amount.of respect for him..And I went to see him.a few years ago.and I was talking to him.for about 30 minutes.about why I thought they should be.using the cloud more meaningfully..They were barely using.the cloud at that point..And he listened to me.and when I was done he said,.“Look, I agree with everything.you just said, Andy,.I agree that we could be inventing.at a much faster clip.but that will be.the job of the next CIO..It will not happen on my watch.”.And that’s what happened..It took him a year or two..He retired, they hired a new CIO,.that CIO said: “What are we doing?”.And they’ve significantly.moved to AWS in the cloud..But they lost two, three,.four years of inventing.on behalf of their customers.and you have to make sure that.you have got builders.who are curious about learning.who are excited about.leaning forward and inventing.and reinventing.their customer experience..Now, you want builders.and talent that’s hungry to invent.but you want to make sure.you guard against the opposite.which is that you have people.who actually solve problems..That you want people.to solve problems for customers.as opposed to solving problems.because they like the technology.and they think it’s cool..And you see this a fair bit..You know if you look in.the enterprise technology space.there are some providers.who are competitor focused..They look at what.their competitors are doing.and they try to fast follow.and one up them..We have a competitor like that across.the lake from us here in Washington..Then you have a number of other.providers who are product focused.and they say look, it’s great.that you have an idea.on a product Mr and Mrs Customer.but leave that to the experts..And that’s the group that you have.got to be careful about.because they often are building.things that they think are cool.as opposed to what really solves.the problems for customers..At AWS, we’re customer focused..What we build is driven.by what you tell us matters to you..And even if you can’t articulate.a feature we’ll try to read.between the lines, understand.what you’re trying to build,.and invent on your behalf..And so if you think.about over the years of AWS,.we have built a lot of technology.that we believe is pretty cool,.you know pretty ground-breaking.stuff, S3 and EC2.and RDS and Aurora.and SageMaker and Redshift..I mean a whole host of technology.but we never built it.because we thought it was cool..We built it because we knew.it would enable you.to build new experiences.and change your business..You’ve got to make sure that.your scarce resource of engineers.are working on problems.that really matter to your customers..The fifth thing is speed..Speed disproportionately matters.at every stage of your business.and in every sized company..And I think that a number of leaders.at enterprises.have resigned themselves.that they have to move slowly..It’s just the nature.of how big they are..It’s the nature of their culture..They have engineering teams.that tell them.“Hey look, this is too risky..This is too big a lift.”.Sometimes you control those teams.into trying something.and the first sign of a problem.they throw up their arms.and say “See.”.Speed is not preordained..Speed is a choice..You can make this choice.and you’ve got to set up a culture.that has urgency.and that actually wants to experiment.because you can’t flip a switch.and suddenly get speed..It doesn’t work like that. You’ve got.to build muscle to get speed..You’ve got to be doing it.all the time..And there is going to be time….Frankly I think that time.is happening right now..It happens a lot more frequently.than most companies realize..But there is going to be.seminal moments.where if you don’t have the ability.to have speed you will not be able.to reinvent when you need to..Now one of the enemies of speed.is complexity.and you have to make sure.that you don’t over complexify.what you’re doing..When companies decide.to make transformations.and big shifts a huge plethora.of companies descend on them.and providers descend on them.and tell them all the ways that you.have got to use their products:.“You need to use us for this.even if you’re using these people.for these three things, use.for these two.”.This company says: “Use us for this.”.They don’t deal with the complexity.that you have to deal with.in managing all those different.technologies and capabilities..The reality is for companies that.are making big transformations.and shifts, it is much easier.to be successful.if you predominantly.choose a partner.and you learn how to do it.and you get momentum.and you get success and you get.real results for the company..Then later on if you want to layer.on complexity.and more providers,.you should go for it..But it’s not a great way.to start to a reinvention.to have too much complexity upfront..And then one of the ways to help you.avoid some of that complexity.is making sure.that you use the platform.that has the most capabilities.and the broadest set of tools..Now I have played a round of golf.with somebody who used a 5-iron.for every shot in the round..It’s doable. It was not pretty.and it was not very effective either..I don’t recommend it..And when you think about the cloud,.because all the services you only pay.for as you consume them -.you don’t pay for it upfront -.Why would you possibly go.with a platform.that has a fraction.of the functionality of a leader?.If you go with the platform.that has the most capabilities.and gives you the right tools.for the job you need to do,.it not only makes it easier.for you.to migrate all your.existing applications,.but also to enable your builders.to build anything they can imagine..And there’s nobody who is close.to the capabilities.across the cloud infrastructure.technology platform as AWS..And you can see that whether.you are talking about compute.or storage or database.or analytics or machine learning.or the edge or IoT or robotics,.in every one of these categories.you get a lot more functionality.in AWS than anywhere else..The eighth key is something that.really wraps all of this together,.which is that the leadership team has.to build aggressive top-down goals.that force the organization.to move faster.than it organically otherwise would..And there are lots of examples.of this..I would like to talk about GE,.where about ten years ago their CIO.decided that they were going to move.50 applications to AWS in 30 days..And her entire team told her.what a terrible idea this was.and she listened to them.and she said,.“We’re doing it anyway.”.They got to about 42 in 30 days..But in the process they figured out.their security and governance model..They figured out how to operate.in the cloud.and they had success.which built momentum.and the ideas came flowing.in such that she could set.that second big top-down goal.to move 9000 applications to AWS.in a few years..Capital One did the same thing.where they set.this big audacious goal top-down.goal: They were going to reinvent.their consumer.digital banking platform on AWS..And then that was on their way.to moving.everything to the cloud in AWS..Setting an aggressive top-down goal.forces the organization to understand.that they are not going to be able.to dip their toe in the water.for a number of years..That you mean business.and you’re going to make this change.and setting up.the right mechanisms to inspect.whether you are getting.the right progress,.and if not,.getting the issues on the table.so you can solve them.is really important..Now, you will notice that most.of these keys, all of these keys really,.except for maybe one,.I mentioned are not technical..They are really about leadership..And so you’ve got to make sure.that you embrace these types of keys.to build a reinvention culture.like you can..It’s very doable.but you have to embrace them..My first speaker today.has embraced these keys.and it’s really remarkable to see.how she has led her company.to start reinventing on top of AWS..It’s my privilege to welcome to.the stage the CIO of JP Morgan Chase,.Lori Beer..[applause].Thanks Andy, it’s great to be here..JPMorgan Chase serves customers.and clients from around the world.from individuals.and their local communities.to corporations and governments..We have an over 200-year history.built on trust..A foundation that allows us to build.simple and intuitive experiences..Those solutions which are.increasingly technologically driven.represent the complete spectrum.of financial services..From lending to banking, markets.to advisory,.and everything in between..And everything we do we do.at tremendous scale..We have $28 trillion in assets.under custody.and process $6 trillion of payments.daily through our Consumer.and Community Bank..We have a relationship.with 50% of US households.and we serve 54 million.active digital customers..We have long realized that while our.relationships and financial expertise.are paramount it is technology that.continues to help us differentiate..With a 200-year history we’ve been.dealing with technological change.since the time.of Thomas Edison literally..The firm financed Edison’s efforts.to invent the first light bulb.and our former Wall.Street Headquarters.was the first office in Manhattan.to draw on Edison Electricity..We were the first bank.to offer ATMs.and our industry.embraced early enterprise.computing to process transactions..We were quick to offer online.banking services.during the rise of the internet.and our industry.is increasingly driven.by mobile computing today..Simply put, we’ve been reinventing.the financial services.industry for decades..But it’s different today..The pace of change has accelerated..Innovation is more rapid.and cloud platforms.are disrupting business models daily..So we decided to completely rethink.our environment to embrace.a true modernization effort.across 250 thousand employees,.35,000 developers,.6,000 applications.and 450 petabytes of data..We asked ourselves how do we leverage.our history of innovation.and institutional know-how in order.to evolve our business.for today’s technology revolution?.We set the direction.at the top of the house.with business.and technology leadership.agreeing on a key vision..We will have the best tech talent..We will own our destiny.in a hybrid cloud world..And we will work with leading.technology companies like AWS.to deliver unique innovation..We’re in the midst of a truly.one-of-a-kind transformation..Leveraging AWS and modern.engineering practices.like refactoring our applications.to be cloud native,.leveraging more advanced.AI and analytics.than we ever have before,.and doing all of this securely..AWS.is helping us along this journey..We’ve benefitted from their breadth.of services and capabilities..Understanding of the enterprise.and willingness to innovate.and collaborate.on key strategic initiatives..We work together.on a unique approach.holding actual hackathons.with AWS and JPMorgan.Chase engineers to decompose.applications and migrate workloads..This process helped us.uncover problems,.gain institutional knowledge.and enhance our collaboration..We’ve developed repeatable blueprints.helping developers.architect modern applications.in safe repeatable environments.using the depth.of AWS services..We are a systemically.important institution.serving clients through some of.the most turbulent events in history..Working with AWS allows us to scale.massive volumes like Amazon EMR.for trading analytics or AWS Lambda.and Amazon Elastic Kubernetes.Service for risk calculations.so we can innovate.to stay.ahead of our competitors..Transforming yourself.takes a lot of work.but we are creating.paved ways for developers.and accelerating adoption..Our developer community is now.innovating at scale using AWS.and we continue to migrate.critical workloads.that can take advantage of the unique.capabilities of the platform..How can we transform our business.through AI and better analytics?.As I mentioned earlier.we have customer relationships.with half of all US households..The scale of our reach provides.an opportunity to use analytics.to better service them..To do that we need.a modern AI platform.that was secure and one that enabled.rapid experimentation..That’s why we built our firm-wide.AI platform OmniAI on AWS.using Amazon SageMaker.for machine learning..SageMaker has helped us.create a platform.to rapidly test and train machine.learning algorithms.enabling us to run.more experiments with more data..These are real use cases.providing real value..As I speak, our data scientists.are leveraging our OmniAI platform.to develop new capabilities..For example, incorporating.natural language.processing to make client.interactions more personalized..Testing advanced machine.learning models.to have a more.comprehensive view of risk..And performing.real-time coaching.and recommendations.for call center agents.so they can better.serve customers..We use SageMaker across.the model development lifecycle.from data labeling.to model selection.to experimentation.and model serving..Our success with.model development on AWS.has influenced.our data management strategy..We’re now investing in cloud data.warehousing technology.with Amazon Redshift.to more effectively scale.our analytic capabilities.in a modern environment..The combination of a scalable.AI platform.and AWS’s Elastic Compute.environments will help us.accelerate our efforts to infuse.analytics in everything we do..I will close with.what I started with..We are a business of trust..Everything we do is for the benefit of.our customers and clients.and we deliver new innovations.in a safe and secure manner..This is why we devote.significant resources.and collaborate with AWS to protect.and continuously.improve the security.of our systems..Our adoption of cloud technology.is already paying off..We’re more agile..We’re more secure.and we’re more efficient..And through this journey.we will work with AWS.to reinvent ourselves and build.financial services of the future..Thank you..[applause].Thank you, Lori..It is really an honor for us.to work with JPMorgan Chase..The partnership.has come a long way.and we’ve just gotten started with.respect to what we can do together..So thank you very much..So I mentioned earlier that when.we think about the key.to reinventing.it’s a combination of building.the right reinvention culture.and then also having.a set of technologies.that you know are available to you.that you jump on to use to reinvent..So I thought I would spend.some time.talking with you now.about some of the areas.that we believe are actively.being reinvented.that will allow you.to do this reinvention..And at the beginning.of each of these sections.I am going to ask the customer.to say a few words.about what they think about.this particular area.and we’re going to start.with the SVP of Engineering.at Snap, Jerry Hunter,.who will talk about Compute..[music].All right we’re going to try.and shoot this on Snap..I remember when we first.started doing Compute -.that was the biggest thing.to hit tech..It disrupted so many parts.of the industry.and I really enjoyed.being a part of it..Joining Snap where the company.is cloud native.and taking advantage.of every innovation.including things like.Graviton2 and Serverless..Every time a new innovation comes out,.we are one of the first to adopt it.because it lowers cost.and improves performance..You might think that.there isn’t much left to reinvent.when it comes to Compute.but the innovations just keep coming..[applause].Thanks, Jerry, we really appreciate.the partnership with Snap.and we appreciate you.continuing to move to AWS..And Jerry knows what he is talking.about as it relates to Compute..He spent a lot of time.at Sun in the early years.and then he was at AWS.where he ran infrastructure.which are our data centers.and our network and our hardware.before going to Snap.to run their engineering..So he’s kind of seen the rise.and change in Compute..He’s absolutely right that.Compute is continuing.to be reinvented as we speak..There’s three major modes.of Compute that we see..Instances, which is.the traditional way.that people have run.Compute - particularly.when they want to get.all the resources.on a box for their application..And then smaller units of Compute.like containers,.where people build on these.smaller microservices.because it lets them move faster.and be more portable..And then event-driven serverless.computing,.when they don’t want to worry.about servers or clusters at all..And these three modes of Compute.are here to stay..They are going to be here.for a long period of time..Let’s start with instances..If you look at Amazon EC2,.which is our service.that vends instances to you.we not only have the broadest.array of instances.but we also have the most powerful.instances within those families..These are things like the fastest.networking instances.where we have 100 gigabits per.second in all our recent generations.and up to 400 gigabytes.per second in our P4d’s..We have the largest high memory.instances at 24 terabytes for SAP.use cases.in our high memory instances..We have the largest local storage.instances with our D3en instances.which are launched today.with up to 336 terabytes..We have the most powerful machine.learning training instances.with our P4d’s..We have the most powerful machine.learning inference instances.with our Inf1 instances..We have the best price.performance graphics.instances with our G4ad instances.which are coming in a week..We’re the only ones.who give you a macOS instance type.which we just launched last night.with our macOS EC2 instances.that lets Apple’s millions.of developers.now leverage the cloud.much more easily..We’re the only provider.that gives you the ability.to run instances.with Intel, AMD, and ARM chips..It’s a very different set of.capabilities than anybody else has..And we’re also iterating.and innovating in a much faster clip..And people often ask us -.they say well,.how are you innovating.at such a rapid clip right now?.And there are really two reasons..The first is what we’ve done.with something called Nitro..We spend five years rebuilding.our virtualization layer.and our Compute platform,.and we launched in 2017..What we did with Nitro was we took.the virtualization of the security,.Networking, and storage.off of the main server chip.and we put that.on our own Nitro chips..And what that did for customers.was it meant.that you got all of the CPU.to run your instances.so you get performance that’s.indistinguishable from bare metal.but at a lower price..And it also meant you got.a stronger security posture.because it used to be that.when you have to troubleshoot VM’s.you had to worry about.whether somebody would do something.to the main server.with your instances..But because the security now.is on that separate Nitro chip.you don’t have to worry about that..What it did for us was, because.we broke out a bunch of those pieces.into separable cards or chips,.it meant we didn’t have.to make changes.in making all these different.things change and evolve in lockstep,.which allows us to innovate.at a much faster clip..Now what that means for you.is that you get instances now.instead of every two to three years -.you get innovative brand now.change-the-game types.of instances in months..That’s a big difference..The second thing that has happened.that has allowed us.to innovate at such a rapid clip.is what we have done with chips..And we have a deep relationship.with both Intel and AMD.and we will.for the foreseeable future..However, we realized a few years ago.that if we wanted to continue to push.the envelope on price performance -.which you have asked us to do -.we knew we were going to have.to develop some of our own chips..And so we acquired a company called.Annapurna who were sophisticated.and very experienced chip.designers and builders -.and we put them to work..And we tried to pick chips that will.allow you to get the most done -.have the most impact.for you business..And so what we started with was.we started with generalized Compute.and we had the team build a chip.on top of ARM.that we called Graviton..And so Graviton first manifested.itself in these A1 instances we had.and they were really.for scale-out workloads,.things like Web tier workloads.and things of that sort..And people loved them..They used them a lot quicker.than we ever imagined and they said:.“Gosh if you would make the chip.more powerful so we could use it.for more of our workloads that would.really change the game for us.”.And so that’s what the team.did with their second version.of the Graviton chip,.which we call Graviton2,.which gives customers 40%.better price performance.than the most recent generations.of the x86 processors.from other providers..And those manifest themselves on C6g.instances and M6g.instances and R6g.instances and T4g instances,.40% better price performance,.that is a big deal..Think about what you can do.for your business if you have 40%.better price performance.on your Compute..And customers have loved this..We have a large number of customers.who are already using it.from Nielsen to Netflix to Snap.and Lyft and NextRoll,.and we are announcing today.a brand new Graviton2 instance.which is going to be.the C6gn instance,.which is our Compute heavy.and networking heavy instance.with a 100 gigabits per second.that will be coming.in the next week or two..We are not close to being done investing.and inventing with Graviton..People are seeing a big difference..Let me give you a couple of examples..Here's a company called Honeycomb.io.and this is a blog that they wrote..And Honeycomb.io is a company.that offers a comprehensive.debugging tool for development teams.and they moved over.to the M6g instances..And you can see they are saving 40%.on price performance.for their Compute.and using a lot fewer instances.than they were using before..Or if you look at NextRoll, which.is a leader in digital advertising,.they are saving 50%.on price performance..This is big deal and so Graviton.is saving people a lot of money..Very excited about it..We have a lot more.coming in this space..Then we asked ourselves what else.can we apply our chip team.to that would solve problems.that we know.are big growth areas for customers?.And what we looked at.was we took machine learning..That’s the other area.that’s growing unbelievably quickly..And what you often see.with machine learning.is that people talk.about the training.because we’re still in the relatively.early stages of machine learning so,.so many people are just.getting their models trained..And there’s a lot of effort.on machine learning training..But if you have models.that work at scale,.you know, if you take for instance.Alexa, where it’s a big old machine.learning model.that we have to train periodically..But we’re spitting out.predictions and inferences.by the millions every hour..And so it turns out that 90% of.your cost is not in the training..It’s in the inference.of the predictions..And nobody was focused to try.to help customers save money.and be efficient there..So we built an inference-focused chip.which we announced last year.at re:Invent called Inferentia..And if you look at the number.of customers using Inferentia.that also was really swelling..But it's interesting to see -.if you want to take a big.use case just look at Alexa..Alexa today has moved 80% of.their predictions to Inferentia..They are getting 30% better cost.and 25% better latency.than they got from their prior chip..That’s a big deal..Now while we focused on inference.we haven’t forgotten.about machine learning training..And we continue.to iterate there as well..Of course we have these P4 instances.which are the most powerful machine.learning training instances.in the cloud,.but people understandably.still want to find ways.to be cost effective.when they are training models..So today I have two announcements.to share with you.that aim to help there..The first is that we will offer.next year, in the first half.of the year, Habana Gaudi-based.Amazon EC2 instances..[applause].And so this is a partnership.between AWS and Intel.and it will use Gaudi accelerators.that will provide 40%.better price performance.than the best performing.GPU instances we have today..It will work with all the main.machine-learning frameworks PyTorch.as well as TensorFlow,.and it will be available.in the first half of the year..Now like with generalized Compute.we know that if we want.to keep pushing that price.performance envelope on machine.learning training we’re going to have.to invest in our own chips as well..And so I am excited to announce.as well AWS.Trainium, which is our machine.learning chip that’s custom.designed by AWS to deliver the most.cost-effective training in the cloud..[applause].So Trainium will be even more cost.effective than the Habana chip.I mentioned. It will support.all the major frameworks -.TensorFlow and PyTorch and MXNet..You will get to use the same Neuron.SDK that our Inferentia.customers use..So if you use.Inferentia for inference.it will be easy.to also get going on our machine.learning chip - on Trainium..It will be available.both as an EC2 instance.as well as in SageMaker.and that’s coming the second.half of 2021..So when you look.at the unmatched array of instances.that you have in AWS.coupled with the relentless.innovation in chips,.and in the virtualization layer,.and the Compute platform,.you’re now getting.reinvented instances every few months.instead of every few years.which is a big deal..Now one of the things that’s really.interesting that we’ve seen.in the last several years.is that people are moving to smaller.and smaller units of Compute..And what I mean - there really.are containers and serverless..And so I will start with containers..People really love containers..It allows them to build.on these smaller chunks of Compute.which lets them move faster.as well as be more portable..And if you look at the growth.in containers in computing.it’s pretty astounding..And the vast majority of it continues.to run in the cloud on top of AWS..Of the containers that run in.the cloud, about two thirds run on AWS -.and that’s because while most other.providers have one containers.Offering - typically.a managed Kubernetes offering -.AWS has three. If it turns out.that what you value most.is using the open source.Kubernetes framework,.then we have our.Elastic Kubernetes Service, EKS..If it turns out what you value most.is having the deepest integration.with the rest.of the AWS platform,.you use our Elastic Container.Service, or ECS, which we can do.because since we control it.we can make sure.that everything launches integrated.with ECS right from the get-go..If what you value most in containers.is running containers.without having to worry about servers.or clusters,.then you use AWS Fargate,.which is our serverless container.offering which nobody else.has anything like..And so one of the things.that was interesting.when we launched containers.and we launched these offerings -.we wondered once we had.a managed Kubernetes service.would people use the other offerings?.Or do Kubernetes.have so much resonance.that people would only use that?.And what we found is that all three.of these container offerings.continue to grow like a weed..Unbelievably fast..If you look at ECS we have over.100,000 active customers using it..We have billions of Compute hours.on EKS run every week on AWS..And if you look, most of the net-new.container customers in AWS.start with Fargate.because it’s so easy to get going..And we actually have.a lot of customers who use two.or even.three of these container offerings.because different teams.have different preferences.and have different use cases..You want the right tool.for the right job..You don’t want one tool.to rule the world,.because there are lots of different.teams and preferences and use cases..So if you look at the problems.that people who use containers.are trying to solve today.they say it’s wonderful.that you have these three offerings..It gives me so much more choice..I can use different offerings.for different use cases..However, I still have.a lot of my containers.that I need to run on-premises.as I am making this transition.to the cloud..And so people really wanted.to have the same management.and deployment mechanisms.that they have in AWS.also on-premises - and customers.have asked us to work on this..And so I am excited to announce.two new things to you..The first is the announcement.of ECS Anywhere,.which lets you run ECS.in your own data center..[applause].So ECS Anywhere allows you.to have all the same AWS-style APIs.and cluster.configuration management pieces.on-premises.that you have in the cloud..So it makes it easy as you’re making.this transition, to be able to run -.if you’re running ECS on AWS.you can run it on-premises as well..It works with all of your.on-premises infrastructure..So not surprisingly when ECS.customers hear that we’ve got.ECS Anywhere they say, “Well,.what about Kubernetes?”.So I am also excited.to announce Amazon EKS Anywhere,.which lets you run EKS.in your own data center..[applause].So again, just like with ECS.Anywhere, EKS Anywhere.lets you run EKS in your data.centers on-premises alongside.with what you’re doing in AWS,.it works again with all of your.on-premises infrastructure..And it’s interesting when we’ve.talked privately to customers.about EKS Anywhere,.they have been very excited.and they’ve said,.“Well, I know that both.ECS Anywhere.and EKS Anywhere are coming in 2021,.but I want to actually get started.getting ready for EKS Anywhere.".And so what we’re also.announcing today.is that we’re going to open source.the EKS Kubernetes distribution.to you.so that you can start using.that on-premises..It will be exactly the same.as what we do with EKS..We will make all the same patches.and updates.so you can actually.be starting to transition.as you get ready for EKS Anywhere..So I think our container customers.are going to be excited by ECS.and EKS Anywhere..But what we’re also starting to see.in addition to huge amounts.of containers growth and adoption.is that more and more customers.are using event.driven serverless computing..And we pioneered this concept.a few years ago with Lambda..And the problem that.we were trying to solve.was that we had some customers.who said:.“Look, I have certain workloads.when something happens,.it triggers needing.to spin up some Compute.and it forces to spin up.EC2 instances.and multiple availability zones.for fault tolerance,.and then they only really run these.jobs for a few hundred milliseconds,.but I have to leave them.all up all the time.because I don’t know.when the jobs are going to come in.”.And they wanted us.to solve that problem.and that’s why we built Lambda..Lambda lets you set a trigger.with a few lines of code..It spins up Compute..We spin it up for you..We spin it up in a fault tolerant way,.and then we spin it back down.when you are no longer running jobs.and then we bill you in increments.of 100 milliseconds..By the way, I am going to announce.right now.that we are changing the increments.in which we’re billing.from 100.milliseconds to 1 millisecond,.which means for a number.of these workloads customers.will be able to save up to 70%..Customers have really loved.this event-driven computing model..They say, “Look I love this..I don’t want to actually just do it.for these use cases where I spin up.a little bit of Compute..I actually would like to have.my architecture.for applications run that way.”.And so we started actually.building other services.that allowed you.to run serverless end to end..These are services like API Gateway.and EventBridge and Step Functions..And then we added triggers.in a lot of our AWS services.so that you could actually.trigger serverless actions.from those services..We have them now in 140 AWS services,.which is seven times more.than you will find anywhere else..And just to give you a sense.of the type of momentum.that you are seeing with serverless.right now, if you look inside Amazon.and you look at all the new.applications that were built in 2020,.half of them are using Lambda.as their Compute engine..That’s incredible growth..Hundreds of thousands of customers.now are using Lambda..And so Lambda is growing.really quickly..Serverless is growing really quickly..Containers are growing really quickly,.and not surprisingly.a lot of customers.are using both containers.and serverless together..And they’ve said, “I really wish that.you would make it easier for us.to run these two smaller units.of Compute together.than you make it today.”.And they asked for a couple.of different things..The first thing they said was.“Look, our usage and adoption.in containers is a little bit.earlier than it is for serverless” -.in part because it looked.a little bit more like instances.and the tools were set up.a little bit better.than they were.for serverless early on..But we’re growing like gangbusters.on serverless and Lambda..And so they said,.“Look, we’ve invested so much time.and energy.into these container images.that we deploy from,.why can’t you just make it.so we can deploy Lambda functions.from these container images?”.So I am excited to announce today.the launch of Lambda Container Support,.which lets you build.Lambda-based applications.using existing container.development workflows..[applause].So now you can.package code dependencies.as any Docker container image.or any Open Container.Initiative.compatible container image,.or really any third-party.based container image,.or something that AWS has maintained.as a base container image..It totally changes your ability.to deploy Lambda functions.along with the tools.that you have invested on containers..So I think customers.are going to find this very handy..Another big challenge.they asked us to try to help solve.is this issue of trying.to manage the deployment.of the smaller units of Compute.where you end up in these situations.where you have all of.these microservices that have to be.deployed together.that comprise an application..And it’s actually quite difficult.to do.if you think about it -.it’s different than instances..With instances, typically you.build it as a single block of code..You have code templates you use.like CloudFormation.to provision the infrastructure as.code, or services like CodePipeline.that do the CI/CD for you or you.do monitoring with CloudWatch..Once it’s set up it doesn’t change.that much. And code is.usually maintained.as kind of a single release,.so it stays pretty coordinated..And there are tools today that.make this pretty straightforward..But if you look at containers.and serverless,.these apps are assembled.from a number of much smaller parts.that together comprise.an application..It’s actually hard, you know,.if you look.at each of these microservices.they have their own code templates.and they have.their own CI/CD pipelines..They have their own monitoring and.most are maintained by separate teams.and it means that there’s all.these changes happening.all the time.from all these different teams..And it’s quite difficult.to coordinate these.and keep them consistent -.and it impacts all sorts of things.including quality and security..And so there really isn’t anything.out there that helps customers.manage this deployment challenge.in a pervasive way..And so this is something that.our team has thought a lot about..And I am excited to announce.the launch of AWS Proton,.which is the first fully.managed deployment service.for container.and serverless applications..[applause].So this is a game changer.for managing.the deployment of microservices..Here’s how it works..A central platform team.or anybody central to an application.will build a stack..And a stack is really a file.that includes templates that use code.to define and configure.AWS services used in a microservice,.including identity,.and including monitoring..It also includes a CI/CD pipeline.template that defines.the compilation of the code.and the testing.and the deployment process..And it also includes a Proton schema.that indicates parameters.for the developers that they can.add, things like memory allocation,.Or a Docker file,.or something like that..Basically, everything that’s needed.to deploy a microservice.except the actual application code..Then the platform -.the central platform team -.will publish the stack.to the Proton console -.pretty often they’re going.to publish lots of stacks.because there’s so many different.use cases for microservices..And then when a developer is ready.to deploy their code.they will pick the template.that best suits their use case, plug.in the parameters they want,.and hit “Deploy”..And Proton will do all the rest..It provisions the AWS services.specified in the stack.using the parameters provided..It pushes the code through the CI/CD.pipeline that compiles and tests.and deploys the code to AWS services..and then sets up all.the monitoring and alarms..Proton also lists in the console.all the downstream dependencies.in a stack..So if the central engineering team.makes some kind of change.to that stack,.they know all the downstream.microservices teams.that need to make those changes.and can alert them and can track.whether or not they made it..This is a game changer with regards.to deploying containers.and serverless apps..We’re very excited about this.and I think our customers.are going to be as well..So think about ten years ago,.if you flash back ten years ago,.the CPU and GPU providers effectively.didn’t have any competition.and it meant that you were able.to get new instances.every two to three years or so..Look at today..Look at what Graviton.is doing with 40%.better price performance on your most.recent generations of x86 processors..It totally changes.what you can get done Compute-wise..Or look at what’s happening.with Inferentia and some of the machine.learning training chips.that I talked about earlier..You are going to get new instances.that allow you to reinvent.your business every few months.now instead of every few years..Ten years ago, we weren’t really.talking very much about containers.or serverless at all..Look how fast those are growing..And if you think about it, if you.think about these container offerings.and how many you have.available on AWS.and the ability now to be able to.manage them the same way both in AWS.as well as on-premises,.and you think about what’s.happening with serverless.and you think about the tools now.that are allowing you.to use these new smaller units.of Compute.and to be able to deploy.much more easily,.there is an incredible amount.of reinvention happening in Compute..We are not close to being done.reinventing in Compute..So you can expect.a lot more to come..Now Compute is obviously.being reinvented,.but so is data and data stores.in a very big way..And to share some thoughts about.what he is seeing in this space,.and he has been there since.the very start of the cloud,.it’s my pleasure to welcome the CEO.and Founder of SmugMug, Don MacAskill..[music].The launch of Amazon S3 in 2006 was.a seminal moment for data storage..This invention totally changed.our trajectory at SmugMug..Today we use almost everything.AWS has to offer..They’ve continued to add more and.more data stores and analytics tools.that have let us.really pick the best tools.for the best job.to serve our customers..Tools like Athena, Redshift,.Elasticsearch, Kinesis, and DynamoDB,.which allows us to scale.and serve photos.at billions of requests per day..The cloud has totally reinvented.how we store, secure, analyze,.and share data at a scale.that we couldn’t have.even imagined 14 years ago..[applause].Thank you, Don..SmugMug was the very first.big AWS customer.and I remember.in the early days of AWS -.this would have been March 2006 -.shortly after we launched S3..Basically, our only sales-person.at the time, Rudy Valdez,.wrote us an email and he said,.“I just got a call from SmugMug.and they are going to put.6 terabytes of data in S3.”.And we said,.“Wait, terabytes or gigabytes?”.And he said, “No, terabytes.”.We said, “With a T or with a G?”.He said, “No, no, with a T.”.We just couldn’t believe it..But SmugMug and Don have seen.the rise of the cloud.and the change in data.and data stores..And in fact have been a big piece.of informing what we’ve built..They’ve given us great feedback and.we really appreciate the partnership..And Don is correct in explaining.that data and data stores.is radically being reinvented.as we speak..And if you think about it today.with the way that the cloud has made.storage so much less expensive,.and then Compute to do something with.that storage so much less expensive,.it’s astounding how much data.is being created and stored today..Just a couple of data points: Analysts.say that in every hour today.we’re creating more data.than what we did.in an entire year 20 years ago..Or they predict that.in the next three years.there will be more data created than.in the prior 30 years combined..This is an incredible amount.of data growth and the old tools.and the old data stores that existed.in the last 20-30 years.are not going to cut it.to be able to handle this..Every single type of data store.is being reinvented.and will be reinvented.multiple times over..Let me give you a few examples..Let’s start with block storage..So if you look at block storage,.it’s a foundational and pervasive.type of storage used in computing..Unlike object storage which has.metadata that governs the access.and the classification of it..Block storage has its storage.or its data.split into evenly sized blocks.that just have an address.and nothing else..And since it doesn’t have.that metadata it means.that the reads and the writes.and access to them are much faster..And it’s why people use block storage.with virtually every EC2 use case..It’s also why that probably.the very most animated debate.that we had as an AWS team.before we launched AWS.was: Could we launch EC2.with just direct-attached storage.that was ephemeral?.Or could we not launch EC2 until we.had high performance block storage?.And it was a very animated debate,.lots of opinions.and we ultimately kind of figured out.that it was going to take us.two more years to build.to a high-performance block store.and we didn’t wait to give you EC2..So we launched EC2 with just.that direct-attached storage..But we hustled like heck.to get to building a block store.and we launched our Amazon.Elastic Block Store, or EBS, in 2008..It was the first high performance.scalable block store in the cloud.and it gave you an easy ability.to provision what storage you needed.and what IOPS you needed.and what throughput you needed..And then you could adjust.as you saw fit..In 2014, we built the current version.of our general-purpose volume..It was called GP2.which is what the vast majority of EBS.workloads run on top of. Every.imaginable workload you can imagine..The feedback that we’ve gotten.the last year or two from customers.is that, “We love GP2..But if we had a wish list.there’s a couple of things.that we would like from you.”.We’d like one, of course, we’d like.the cost per gigabyte to be less.and then we want.to be able to sometimes scale.throughput or scale IOPS.without also having to scale.the storage with it,.which is what GP2 asks you to do..So the team has been working on this.and I’m excited to announce today.the new version of our general.purpose volumes, gp3,.which allows you to have 20%.better cost per gigabyte.as well as be able to provision IOPS.and throughput separately.from storage..[applause].So the baseline performance of these.new gp3 volumes is 3,000 IOPS.and 125 megabytes per second,.but you can burst that and scale.that up to a peak.of 1,000 megabytes per second,.which is four times that of GP2..And you’ll see that customers.will be able to run many.more of their demanding workloads.on gp3s and they even work for GP2..Yet, there are certain types.of use cases.where you need a lot more IOPS.than you get with gp3s..Cluster databases.are a good example of this..And that’s why we built.our Provisioned IOPS.or io2 volumes for EBS.which give you four times.the amount of IOPS of a gp3 volume..And you can, of course.if you start with gp3s,.and it turns out you think.you need io2s,.you can use.our elastic volumes feature.to seamlessly move that volume..But it’s also true, while people.run their most demanding.IOPS applications.on top of io2s,.that there are still workloads.that are even more demanding..Some of the most.demanding Oracle databases.are SAP HANA databases and offerings.where people need in the neighborhood.of 200,000 IOPS.or 3,000 to 4,000 megabytes.per second of throughput..And it’s true that you can stripe.together.a number of these io2 volumes.to give you more collective.IOPS of throughput.but the more that you have.to stripe together,.the harder it is to manage.and to keep consistent.and to get the performance you want..And so what customers have said is,.“Look, what you’re forcing me to do.for these most demanding workloads,.is you’re forcing me to use.these storage area networks,.or SANs, which really they’re.a bunch of clustered discs.with networking attached to them..You’re forcing me.to run these SANs on-premises.and I don’t want to have to run these.SANs because they’re expensive.”.If you get a good deal on a SAN.it’s about $100,000,.but then when you factor in support.and maintenance,.and maintaining it across.multiple data centers,.you get to $200,000 pretty quickly.and if it turns out you exceed.the capacity of a SAN,.you have to buy an increment.of another couple.hundred thousand dollars..So people don’t like the cost,.but it’s also hard to manage..You have to update.and maintain the software,.you have to do the same thing.with the hardware,.you have to manage it.in data centers..You have to make sure.it’s fault-tolerant..The customers have said,.“Look, you’ve left me with no option.because there is no SAN.in the cloud.” Until now..I’m excited to announce.io2 Block Express,.which is the first.SAN built for the cloud..[applause].So Block Express volumes.give you up to 256,000.IOPS, 4,000 megabytes per second,.64 terabytes of storage capacity..That’s 4X the dimensions of the io2s.on every single one of those..That is massive for a single volume..Nobody has anything close to that.in the cloud.and what it means is that you now get.the performance of SANs in the cloud,.but without the headaches.around cost and management..You just spin up.a Block Express volume,.we manage it and we’ll back it up.and maintain it for you..We’ll replicate it across an AZ..You can use the EBS snapshot.capability to auto lifecycle,.a policy to back it up to S3,.and if you need more capacity,.you just spin up another.Block Express volume.at a much lower cost.than trying to do it.at $200,000 a clip..So, we will add additional.SAN features in 2021,.things like multi-attach.and IO fencing.and make elastic volumes.work with it..And this is pretty exciting..There was a lot of very complicated,.sophisticated,.innovative engineering.to give you Block Express,.but it’s a huge gamechanger for your.most demanding applications.that you want to run in EBS..So block stores are being reinvented..What about databases?.Databases as you all know are right.in the middle of every application.and they’re hard to manage..You have to set them up,.you have to tune them,.you have to patch them,.you have to make sure.you get the right fault.tolerance and performance..And it’s why companies have.so many database professionals.that they have to hire..It’s also why we built.our relational database service,.which is our managed.relational database service.which we launched about ten years ago.and has been wildly popular..But if you look at, despite.the growth of things like RDS,.it’s still true that the overwhelming.majority of relational databases.live on-premises.and they live largely.with these old-guard.commercial grade database.providers named Oracle.and Microsoft with SQL Server..And this is an unhappy place.for customers..We’ve talked about this.for several years..This is why you’re seeing.so much movement..But it’s unhappy because.those offerings are expensive,.they’re proprietary, they have.high amounts of lock-in.and those companies.have punitive licensing terms.where they’re willing to audit you.and if they find any discrepancy,.they extract more money out of you..And these companies.also have no qualms.about changing the licensing.terms mid-stream on you..If you just look at what Microsoft.did with SQL Server.in the last year or two..They basically changed the terms.so you can’t use your SQL Server.licenses anywhere.but Microsoft’s cloud..Is that good for customers?.Hell no. Is that good for Microsoft?.I think they think so..I think it’s short-term thinking.because our experience.over the fullness of time.is that customers flee companies.the first chance they get.when they feel like.they’re being abused..And this is something.that customers are fed up with.and they’re sick of -.and it’s why they’re moving as fast.as they can to these open engines.like MySQL and Postgres..But to get the type of performance.you get in.a commercial grade database.in these open engines,.you can do it, but it’s hard work..We’ve done a lot of it at Amazon.and our customers.asked us to fix that problem for them.and that’s why we built Aurora..And Aurora has 100% compatible.versions with MySQL and Postgres..It has several times.better performance.than those community grade versions..It has at least the same fault.tolerance and durability.and availability.as the commercial grade databases,.but at one tenth of the cost..This is why customers.have been flocking to Aurora.as quickly as they have..It’s the fastest growing service.in the history of AWS..It has been since its launch,.and you see that we have over.100,000 customers now using Aurora..These are companies like Airbnb.and AstraZeneca and BP,.and Capital One, and Fannie Mae,.and Petco, and Verizon, and Volkswagen..One of the great things.though about having a service.that people love so much.and is growing so fast.is that you get a lot of feedback.on what else people would love you.to build and that is fuel for us..Please keep it coming..That is how we choose what to build..And a lot of our Aurora customers.said, “Look, we love Aurora..If we had a big ask it would be that.we’d like to be able to run.Aurora taking advantage.of that serverless architecture.where we didn’t have to think.about servers and clusters.”.And so that’s why we built.Aurora Serverless,.which is really.an auto-configuration for Aurora.which allows you to set up.an Aurora database.and then when you need to scale up.because of capacity,.we’ll scale it up in 5 to 50 seconds,.usually doubling the capacity.each time you need more capacity..And we have thousands of customers.who’ve been using Aurora Serverless.primarily for dev and test workloads..And they said,.“Look, we want to run… We love it..We want to run it.for production workloads,.but we need some things from Aurora,.what we’d normally get from Aurora.if we’re going to do that.”.And they said, “We need Multi-AZ..We need Read Replicas.and then when we need.to scale up capacity-wise,.we need it to happen instantaneously..We can’t wait 5 to 50 seconds,.and we only want to scale up.in the precise increment.that we need to scale up.”.So the team went away and started.working on that this year..I’m excited to announce the launch.of Amazon Aurora Serverless v2,.which allows you to scale to hundreds.of thousands of transactions.in a fraction of a second..[applause].And so Aurora Serverless v2.totally changes the game for you.with serverless.as it relates to Aurora..You can scale up as big.as you need to instantaneously..It only scales you up in the precise.increments that you need to..So if you’re using.Aurora Serverless v2,.you can save up to 90% versus.provisioning Aurora for the peak..It adds in a lot of the Aurora.capabilities people want - and Multi-AZ.and Global Database and Read Replicas.and Backtrack and Parallel Query -.and it really makes.Aurora Serverless v2 ideal.for virtually every Aurora workload..MySQL is available for you now.with Aurora Serverless v2.and Postgres will be available.in the first half of 2021..Now I ask you, how many of these.old-guard commercial grade.database companies would build.something like Serverless v2.that’s clearly going to take.a meaningful amount of revenue.away from their core.database offering?.I wouldn’t hold your breath..I think the answer is none of them..They’re just not built that way..But we have a different way.of thinking about our business,.which is that we’re trying to build.a set of relationships.and a business.that outlasts all of us..And the best way we know of doing.that is listening.to what customers care about..And if we can help you build more,.more efficiently, more effectively,.change your experience.for the long-term,.even if it means short-term pain.for us or less revenue for us,.we’re willing to do it because we’re.in this with you for the long haul..I think that’s one of the reasons.why customers trust AWS in general.and have trusted us in the database.space over the last number of years..If you look at it,.just in the last few years,.we’ve had more than.350,000 databases migrate to AWS.using our Database Migration Service.and the pattern that usually follows,.they say,.“Look, we used the Database Migration.Service to move our database data..We used your Schema Conversion.Tool to convert the schema,.but there’s a third area.that’s making this harder.than we wish it were,.that we want your help with,.and that’s trying to figure out.what to do with the application code.that is tied.to that proprietary database..And so customers have asked us,.“Can you do something.to make this easier for us.because we want to move.these workloads to Aurora?”.And especially with the way.they’ve watched Microsoft.get more punitive and more.constrained and more aggressive.with their licensing, they want help..So the team has been working on this.for a little bit more than a year.and I’m excited to announce today.the launch of Babelfish.for Amazon Aurora Postgres,.which lets you run SQL Server.applications on Aurora.Postgres with little.to no code changes..[applause].So Babelfish is a new translation.capability that lets you run.SQL Server.applications on Aurora Postgres..And what Babelfish.does is it understands.Microsoft SQL Server’s.T-SQL dialect.and it creates a tabular.Data Stream or a TDS endpoint.for your app to connect to,.and it understands Microsoft’s.proprietary schemas..And what it means for you is that now.you can use the Database Migration.Service to move your database data,.the Schema Conversion.Tool to move your schema or convert.your schema, and then you can use.Babelfish to update.your application configuration.to point to Aurora Postgres.instead of SQL Server.and you get to shed those expensive.and constraining SQL Server licenses..Because Aurora now is able.to understand both T-SQL and.Postgres, you can write.application functionality in Postgres.to run side by side with your legacy.SQL Server code..And so customers that we’ve spoken.to privately about this,.to say they’re excited would be.one of the more large understatements.I could make. Very excited..And they were so excited about this.that as we were preparing.for re:Invent and discussing it,.we realized that this was probably.bigger than just Aurora Postgres..That people really wanted the freedom.to move away from.these proprietary databases.and to Postgres which is where.most people move from these..And so we decided that.we’re going to open source.Babelfish. And so I’m excited.to announce Babelfish for Postgres,.which is an open source project..[applause].So Babelfish for Postgres will use.the permissive Apache 2.0 license..It means that you can modify.or tweak or distribute.in whatever fashion you see fit..All the work and planning.is going to be done on.GitHub so you have transparency of.what’s happening with the project..So this is a huge enabler.for customers.to move away from these.frustrated, old-guard proprietary.databases.to the open engines of Postgres..You can sign up for Babelfish.for Aurora Postgres today.and then you’ll be able to sign up.for the open source project in 2021..So block stores are being reinvented,.relational databases.are being reinvented..Heck, actually all of databases.are being reinvented..If you think about it,.we’ve been talking about this.for the last few years..That world that you lived in.for 20 or 30 years.where you used a relational database.for every single workload,.that time has come and gone..If you have data in volumes that are.gigabytes and sometimes terabytes,.you can get away with using.a relational database for everything..It’s not ideal,.but you can get away with it..But in a world where you’re now.dealing with terabytes of data.and petabytes of data,.and sometimes exabytes of data,.a relational database.doesn’t make sense..It’s too expensive,.it’s too complicated,.and it doesn’t perform as well.as purpose-built databases.that do a particular workload.or use case extremely well..And that’s what we’ve been.working on the last several years..We have built seven of these.purpose-built databases,.more than you’ll find anywhere else.by a fair amount,.that allow you to have.the right tool for the right job..So, I’ll give you some examples..If you’re a company like Lyft.and you have millions of geolocation.and driver combinations,.you don’t want a relational database..It’s too complex, it’s too expensive,.it’s not performing..You want a high throughput,.low latency, key-value store.like DynamoDB,.or if you’re a company like Peloton,.you want to show your dashboards.in microseconds,.you want an in-memory database.like ElastiCache..Or if you’re a company like Nike.and you’re trying to connect.all these different graphs.of information.that have relationships.attached to them,.you want a graph database.like Neptune..Or if you’re doing work.at the edge with IoT.where the data’s coming.in a timestream format,.you want a time series database.called Timestream, which we have..If you want to run managed Mongo,.if you want to run managed Cassandra,.you want those databases.that allow you to have the right tool.for the right job..I think you’re seeing.the same exact thing happening.with purpose-built.analytics stores..So if it turns out.that you want to do.querying directly on your data lake,.or in S3, you use Athena..If it turns out that you want.to actually process vast.amounts of unstructured data.across dynamically scalable clusters.using popular distributor frameworks.like Spark or Hadoop or Presto,.you use EMR..If you want to do large-scale.analytics on log data.for operations you use.our Elasticsearch service..If you want to do real-time.processing and streaming data,.you use Kinesis,.and if you have structured data.where you need super-fast.querying results,.you want something like.a data warehouse;.You want Redshift, which was the first.data warehouse built for the cloud -.continues to be the largest data.warehouse in the cloud -.and they continue to innovate.at a rapid rate..Last year at re:Invent you saw.the RA3 instances,.which separates storage from.the compute, which people have loved..We’re just around the corner from.the general availability of AQUA.which moves the compute.to the storage.and will give you.10x better query capabilities.in terms of speed than anywhere else..You want these.purpose-built databases.and these purpose-built.analytics stores..And we see customer flocking to them..Now, it’s actually brought up a.really interesting challenge.and question.for customers which is: Most companies.either have a data lake.or will build a data lake to take all.that data from disparate silos.and move it together,.so you have one place.where you can do your analytics.and your machine learning from..And most of those are built on S3..We have tens of thousands.of data lakes built in S3,.more than you’ll find anywhere else.because of the security.and reliability and governance.and compliance capabilities,.and the broad features.and the flexible.and cost-effective performance of S3..So,you can have.these data lakes.that you centralize all your data,.so you can run analytics.and machine learning from,.but as we just talked about,.you’re increasingly seeing customers.using more and more.of these purpose-built data stores..And so customers say,.“Well, I want my data.to be able to move back and forth.between these different stores.because it’s very useful to take.some of these views that I have.and use them in other spots.”.And while we have capabilities.in many of these services.that let people move them.back and forth,.it’s really not easy enough.for people to do,.such that they do it.in a pervasive way..So customers really,.really want more freedom.for their data to move around..Our team’s worked on this.for the better part of a year,.and I’m excited to announce today.the launch of AWS Glue Elastic Views,.which lets you easily.build materialized views.that automatically can find.and replicate data.across multiple data stores..[applause].So, Glue is AWS’s ETL service,.and you can write SQL.or you can use our Glue studio.visual tool.to do extracting.and loading and orchestration..And we recently a few weeks ago.launched something called DataBrew.to make it easy to clean.and normalize data with no coding..But Elastic Views is different..What it lets you do.is it lets you write a little of SQL.to create a virtual table.or a materialized view of the data.that you want to copy and move.and combine from source data store.to a target data store..Well in the old days you’d have.to figure out a way to write code.and make sure you can get.all the DynamoDB attributes.and figure out how to move that,.and move it in sync,.keep it up-to-date.as the attributes changed.and new ones came in.or things got adjusted in any way..All of that muck is taken away.by Elastic Views..And so what Elastic Views does is.it allows you.to set up a materialized view.to copy that data.and move that data.from one of those source data stores.to a target data store.and then it manages.all of the dependencies.of those steps as they move..And if something changes.in the source data store,.Elastic Views takes that.and automatically changes it.in the target store in seconds..If it turns out, for whatever reason,.the data structure changes.from one of the data stores,.Elastic Views will alert the person.that created the materialized view.and allow them to adjust that..This is a huge game changer.in being able to move data..It takes a lot of the work.that people had to do –.and frankly just found it.so much work that they rarely did..You know, when you have.the ability to move data.and to have purpose-built data stores.and have a data lake,.but also move that data easily.from data store to data store,.there is a lot of power in giving.that freedom of movement of data.and this is going to be.a big game changer for customers,.having Elastic Views..We’re very excited to give it to you..A company that I think is doing.one of the most revolutionary.and transformational.things around today.and that’s also using data and.computing in very innovative ways –.is what’s happening at a company.called Boom,.which is trying to build the first.supersonic airplane in 60 years..And to share with you.how they’re doing that.and what they’re doing with data.and compute on top of AWS,.it’s my pleasure to welcome.the founder and CEO of Boom,.Blake Scholl..[applause].[music playing].[music].So what does a revolution.in high-performance computing.have to do with a revolution.in high-performance aircraft?.What does cloud computing have to do.with how we fly.through actual clouds?.And why now is.a software engineer building.the next commercial.aircraft company?.Air travel is integral.to our modern lives,.yet aviation is a domain.starving for step-change innovation..The last big new thing.was the jet airliner.invented more than six decades ago..Tokyo to Seattle has been a nine-hour.flight for going on 60 years..At Boom, we are guided.by one fundamental mission –.to make the world.dramatically more accessible..And by the end of the decade,.millions of everyday travelers.will enjoy the benefits.of supersonic flight.aboard Overture, an airliner.twice as fast as any flying today..That means that Tokyo will be just.four-and-a-half hours.from Seattle, and London.just three-and-a-half from New York..Speed unlocks new possibilities.for human relationships.and business connections..That’s why major aerospace players.such as Rolls Royce, Japan Airlines,.and the United States Airforce.are among Boom’s partners..All of this is possible.thanks to advances.in computing that enable.a startup company.to spark a revolution in speed..Twenty years ago, I hit two.life milestones as a new graduate..I started my first job here.at Amazon as a software engineer,.and I started taking flying lessons.just a few miles down the street.at Boeing Field..Around the same time, Amazon was.building the fundamental web services.that would later become AWS,.charting a new future of.computing across all industries..Little did I know.that these things would intersect.so powerfully in my future..I never could have predicted.that the innovations in cloud.computing happening at Amazon and AWS.would fundamentally change.how we all fly..Well fast forward to today.and Boom is designing.and building the world’s fastest.and most sustainable airliner,.and we’ve just announced that.we’re going all in with AWS..Why?.Well it turns out that high.performance.computing is key to this.new era of airplane design..And AWS is the leading.cloud provider,.allowing us to leverage a wide range.of capabilities and services..Plus, Amazon’s relentless focus.on the customer.means that some of.the best minds in cloud.computing are helping Boom innovate.and get to market faster..AWS levels.the playing field in aerospace,.allowing a startup company.to develop.what previously only big companies.or governments could do..So this is how airplanes were.designed before the age of computing..Engineers worked with drafting paper.and slide rules..They built scale models.to test in wind tunnels.leading to a process of iteration.that was slow and costly..Today, to design faster airplanes,.we need the fastest computers. And.computational methods leveraging AWS.save us literally years.of schedule and millions of dollars..Moreover, because we can now test.many designs quickly.and inexpensively,.we can deliver a better airplane..In October, we rolled out.XB-1, history’s first.independently developed.supersonic jet..To design XB-1, we leveraged EC2.to stand up HPC clusters.often with more than 500 cores -.hundreds of possible airplane.designs flew through virtual.wind tunnel.tests encompassing.thousands of flight scenarios..Because AWS allowed us to run many.hundreds of these simulations.concurrently,.we achieved a sixfold increase.in team product productivity..Simply put, without AWS today.we would be looking at a sketch.of a future airplane concept,.not an assembled jet because years.of design work would still remain..Since airplanes are amongst.the most complex machines.ever created by humanity,.Boom will generate petabytes of data.as we design and develop.our Overture airliner..Already we are transferring.525 terabytes of XB-1 design.and test data to AWS..Because they let us.put compute next to data,.we can run models across our dataset.gaining actionable insights..For example, we’re using machine.learning to calibrate simulations.to wind tunnel results,.accelerating model convergence.and allowing us to deliver.a more optimized aircraft..All in all, we have used.53 million core hours in AWS.to design and test XB-1..And in the same manner we expect.to use over 100 million core hours.as we finalize the design.of our Overture airliner..Because our pursuit of speed is about.making earth more accessible,.we’re taking great care.to build an environmentally.and socially responsible.supersonic jet,.and I am proud that Overture.will be 100% carbon neutral from day.one thanks to its use.of alternative fuels,.which means that supersonic flight.is going to be more.affordable than ever before..Well, great revolutions.and fundamental technologies.enable benefits that are difficult.to predict or even imagine..Think of how many industries AWS.has already transformed,.and today supersonic flight.is one of those surprising.benefits of computing..Just as AWS is reinventing computing,.at Boom we are reinventing travel..So what further breakthroughs.will be sparked.by a revolution on how we fly?.At Boom our vision.is one of accessibility,.of new possibilities.unlocked in the world around us..By the end of the decade.your flights will be cut in half..So, what will you do when Australia.is as accessible as Hawaii is today?.And your flight is completely.carbon neutral?.How might you transform.an industry?.What new people could you.come to call friends?.But Overture is merely.the first step.towards our vision.of a supersonic future..Because I dream of a day where you.can go anywhere in the world.in four hours for just $100..Where the fastest flight.is also the most affordable..Boom is making supersonic.flight mainstream and AWS.is helping us deliver.on that promise..I am so excited to see.what you will invent.when more of the world.is within your reach..Thank you..[applause].Thank you, Blake..I’ll tell you what I'm going to do.when Australia.is as accessible as Hawaii –.I’m going to go to Australia more..It’s incredible what Boom is doing..We often talk at Amazon.about thinking big..That is thinking big. It’s very.impressive, it’s very exciting..It could really change life.for all of us.and we’re honored.to be partnering with Boom..You know, I remember a time.about 25 years ago or so,.people thought.that search was boring,.and there was really.nothing left to invent..And I remember a time.maybe 15 years ago.where people felt like technology.and infrastructure was boring,.and there wasn't much left to invent.and those turned out to be wrong..And I think oftentimes people.think of data stores.and databases as being boring.and what is there left to invent..And I hope you can see.the answer’s a lot..You see that block stores.are being reinvented..You see that relational databases.are being reinvented..You see that purpose-built data.stores are being reinvented..And then the movement of that data.between those stores that frees up.the power of what you can build.with Elastic Views….huge amounts of reinvention.happening with data stores..Now hand-in-hand with data.is what has been happening.with machine learning.and the adoption of it..And to start us off on this section.I’m going to transition it over.to the CTO of Intuit,.Marianna Tessel..[music playing].Intuit is an early adopter of AI,.starting the journey.over a decade ago..We see tremendous opportunity.in applying AI.to revolutionize our business.and to benefit our customers..And given the potential,.this is just the beginning..Working together with AWS,.we developed a robust ML platform.that empowers our engineers.to incorporate AI into our products..We use AWS tools.for model development, training,.and hosting, and integrate.our own capabilities.for orchestration.and feature engineering..This has been game changing..Together with AWS.we made great strides.in driving AI/ML.innovation with speed,.helping us deliver.smarter products.faster to more than.50 million consumers,.small businesses, and self-employed.customers around the world..[applause].Thank you, Marianna..It is really interesting.when we talked about earlier.about being able to build.the reinvention culture..Intuit has done that..And you can see it,.not only with how they have.been more sophisticated.and more leaning forward.with respect to using the cloud.for their technology.infrastructure,.but also with respect.to what they are doing with machine.learning where they’re well.ahead of most companies..So people have been talking about.machine learning for over 20 years..In the cloud,.with cost structure around compute.and then the amazing amount.of capacity you have,.has made machine learning.much more practical..And while more.and more companies -.amazing progress - have started.using machine learning,.make no mistake about it,.it is very early in the history.of machine learning.and almost everything.is continually being reinvented..There’s loads of examples..I’ll just give you.one simple one here..So this is the top frameworks.that have been used in new machine.learning scientific publications.in the last five years,.which is often a leading.indicator of what people use..And you heard us.talk a few years ago.when TensorFlow was so dominant.in the frameworks.used that the one constant we see.in machine learning is change..And you can see that..Look at how PyTorch has caught.TensorFlow over the last few years,.because it’s much easier to use.and to get started with..And when we talk to machine.learning practitioners,.90% of them tell us.they use more than one framework.and 60% use more than two..And so clearly you can see.that machine learning.is in the very early stages..Frameworks was one of the most.stable things a few years ago,.but you can see it’s changing.very substantially..Now we have a lot more machine.learning capability.than anybody else.by a fair bit,.and it’s one of the reasons.why last year.in my keynote the section on machine.learning was 75 minutes..I’m not going to do that this year..For the first time, we have broken out.a machine learning keynote that.Swami’s going to do next Tuesday..I’m going to leave a lot.of the goodies for him..But I have a few.that I’m going to share.and I’m going to really.frame them in the context.of the asks we get.from our machine learning customers..The first ask we get is they say,.“Look, we want to have.the right tools for expert machine.learning practitioners,”.and that typically involves.chips and frameworks.and this is the group.of people.by the way who operate at that bottom.layer of the machine learning stack..They’re comfortable.building and training.and tuning and deploying machine.learning models of scale..And we talked earlier.about all the areas.we’re trying to help.our expert machine.learning practitioners with chips.around inference.and machine.learning training,.but I also think it’s important.to think about the frameworks..We took an approach.a few years ago.where we said we’re going to support.every single one of the major machine.learning frameworks and we built….that was a unique approach..Everybody else was just.focused on TensorFlow..And what we did was.we built separable teams..We have one team that’s just focused.on how to optimize TensorFlow in AWS,.one team that’s just optimized.on how to run PyTorch in AWS,.and one that’s just focused.on MXNet in AWS..And that’s why you get.the best performance.in all those frameworks on AWS..I usually show you.benchmarks at this point..I’m going to leave that.to Swami for next week..But we believe that we’re not done.seeing new frameworks pop up.that you’re going.to care about..And the commitment you have from us.is that we will continue.to support every single.one of those major frameworks.because we know that expert.machine learning practitioners.want the flexibility.to build however they see fit..The second ask we get is,.“Look, the reality is there aren’t.that many expert machine.learning practitioners.in the cloud.and there aren’t.that many in the world,.and those that exist tend to live at.the big technology companies.”.So, if we want machine.learning to be as expansive.as we all believe it should be,.you’ve got to make it easier.for everyday developers.and data scientists to be able.to use machine learning..And that’s why we built.a few years ago.SageMaker, which sits at that middle.layer of the machine learning stack..And SageMaker was a step-level.change in the ease.with which you can build,.train,.tune, and deploy.a machine learning model..We have tens of thousands.of customers.who are standardizing on top.of SageMaker and using it..And these are companies.like 3M and ADP and Cerner.and Intuit and GE and Snap.and the NFL.and T-Mobile and Vanguard,.just a broad group..And one of the things.that people like most about SageMaker.is they see how quickly.SageMaker’s continuing to iterate..This is the second year in a row.that the SageMaker team has added.over 50 new features.in the last 12 months..That’s like one a week..That’s really unusual..And we haven’t stopped..If you think about last year.at re:Invent,.I announced in my keynote.the launch of SageMaker Studio.which was the first.Integrated Development.Environment, or IDE,.for machine learning..And we gave you Notebooks,.which were easy to create.in one click and share. And Debugger,.which made it easy.to debug your models..And Experiments, which saved all.the artifacts of your experiments.so you could kind of figure out.what happened and share them..And Model Monitor, so you could tell.if you had model drift, and Autopilot.which looked at automatically.what was in a CSV file.and created a new model for you,.machine learning model.automatically with transparency.and how it was created,.so you could pick it up.when you were done seeing it,.if you wanted to evolve it yourself..That was a huge amount.of new innovation last year.and customers have loved.SageMaker Studio..And they’re using it.in a really expansive way..And what we often do,.again we have services.that have this amount of traction..We get a lot of gratuitous feedback.from customers which we love,.but we also constantly.ask customers,.“What else can we build for you.that would make your life easier?”.And the topic that seems to come up.first and foremost.almost every time is,.“How can you make doing data.preparation for machine.learning much easier?”.And data preparation is hard..If you think about it,.to build a model you’ve got.to collect all this data.from different sources.that come in different formats..None of it’s normalized.which you need for the models..And when you’re building.these models,.you need to look at all.these different dimensions,.what they call machine.learning features..I’ll take something.like a real estate app..If you want to predict.the prices of real estate,.you need to look at features.when you build a model -.like how many bedrooms.or how many bathrooms.or how many square feet.is the house or what are other houses.on that street selling for?.Does it have a Starbucks.within five miles?.These are all features that you need.that arrive in different formats.that the model cannot understand,.where you have to convert.these features into the right format.that the model can understand..This is what is called.feature engineering..And then there are also,.by the way, times.where you want to take.two different features.and combine them, or two or more.different features and combine them..Take something like,.in a real estate example,.you want a house pricing index where.you’re combining different features.that make.the model more efficient..Converting these models,.converting these features,.combining features themselves –.that type of feature engineering.is really hard.and it takes a lot of time..You’ve got to write queries and code.to get all that data.from various sources..You have to convert the data.to the format the algorithms can use..You sometimes want to actually.combine the features..You want to prototype.these features to see.if your feature engineering worked.or not before you actually apply.those transformations everywhere..And then you’ve got to apply.the transformations.and make sure you don't have.missing data or outliers..It's just a lot of work..And people said there must be.an easier way,.which is why I’m excited.to announce today.the launch.of Amazon SageMaker Data Wrangler,.which is the fastest way.to prepare data for machine learning..[applause].So Data Wrangler is a total.game changer.with respect to speed of doing.machine learning data preparation..The way it works.is you point Data Wrangler.at the appropriate AWS data store.or third-party data store.and then Data Wrangler has over.300 built-in conversions.and transformations.that, through machine.Learning, will automatically.recognize the type of data coming in.and suggest the right transformation.for you to make that you can apply..You can of course.do your own thing as well..It also makes it much easier.in the Data Wrangler console.to combine features to build those.composite features I was mentioning..You can preview very easily these.transformations in SageMaker Studio,.and then if you like what you see,.you simply apply that transformation.to the whole dataset.and Data Wrangler.manages all the infrastructure.and all that work under the covers..It’s a total game changer.in the time.that it takes to do data.preparation for machine learning..Now, because you spend so much time.on the data prep.as well as on.the feature engineering,.not surprisingly you want to use.these features.in lots of other models,.so you need a place.to store these features.and make them easily accessible..And this turns out.to be a hard problem..Sometimes you might step back.and say,.“Well why is this a hard problem?.Why don't you just store them in S3?”.The problem is, you may be able.to do that if you wanted to store.as an object maybe.a simple set of features.that are mapped to one model,.but features are hardly ever mapped.to just one model..They’re usually mapped to lots of.models because they are highly useful,.and you did all the work.to get the features in a state.where the model can understand them..And then sometimes you’ve got.subsets in that set of features.that want to be.their own sets of features..And then you’ve got multiple people.who want to access those features.and the different sets.with multiple models,.and pretty quickly.it becomes complicated.and hard to keep track of,.which is why people.often try to keep track of this.in an email or spreadsheets.or sometimes build a clunky UI.that takes time.to really work very well..You also need to… these same features.need to be used to train models.and then also to make predictions..And they’re really.different use cases..When you’re training a model,.you’ll use all the data.in a particular feature.to be able to get the best.model possible to make predictions..But when you're making predictions,.you often will take.just maybe the last five data.points in that particular feature,.but they have to be stored.the same way.and they’ve got to be accessible..When you’re actually using these.features for inference.and predictions,.you need really low latency.because you're trying.to make predictions.in close to real time.in your applications..So it’s really hard to do all.these things in a generalized store..And so that’s why we have built.and I’m excited to announce today.SageMaker Feature Store,.which is a new repository.that makes it easier to store, update,.and share machine learning features..[applause].And so the Feature Store,.it is a purpose-built feature store.that’s accessible in SageMaker Studio.and it makes it much simpler.to name, organize, find,.and share features with teams.because we’ve built.a purpose-built store for features..It turns out it makes it really easy.for features.to actually be accessed.either for training or for inference..Even though they’re different.use cases,.we’ve built a store.that makes that simple..And because Feature Store.is located in SageMaker.close to where your machine.learning models are running,.you get really low latency.on your inference and prediction..So this is another big deal.for people.that are trying to build machine.learning models..Now I think you can tell from.understanding what happens in machine.learning or looking at data.preparation or Feature Store,.that machine learning has a lot.of things that have to happen.sequentially.or sometimes in parallel,.but really lend themselves well.to orchestration and to automation..And this is true in normal code.as well.when you’re building applications..It’s why they’ve built these.CI/CD pipelines..But in machine learning there.is no CI/CD..None of them exist pervasively..People have tried to build their own..They’ve tried to do it homegrown..It hasn’t been that scalable,.it hasn’t worked the way.they wanted it to..And customers want an easier way.to do this..And so I’m excited to announce today.the launch of.Amazon SageMaker Pipelines,.which is the first purpose-built.easy-to-use CI/CD.service for machine learning..[applause].And so with Pipelines you can.quickly create machine.learning workflows.with our Python SDK..And then you can automate.a bunch of the steps.from a number of the things.you have to do on data preparation.in Data Wrangler -.to moving the data from.Data Wrangler to the Feature Store.to some of the activities you want.to take once in the Feature Store,.to training, to tuning,.to hosting your models..You can build all of these things.in a workflow.that happens automatically..And then Pipelines manages.all of those dependencies.between each of the steps.and orchestrates the flow.so you can have any number of these.automated steps.in a Pipeline workflow..We also give you Pipelines.with preconfigured templates.for building and deploying.so that you don't have.to create these from scratch..You can either use those verbatim,.you can use those as a base.and then customize how you see fit,.or you can actually.build them from scratch..You can do all of those things.in Pipelines..And then Pipelines automatically.keeps track of all of the artifacts.and then keeps an audit trail.of all the changes.so it's easy to troubleshoot.if you need to do that..So SageMaker has completely.changed the game.for everyday developers and data.scientists in being able to build,.train, tune, and deploy machine.learning models..And people have flocked to SageMaker,.not only because.there's nothing else like it,.but also because of the relentless.iteration and innovation.that you continue to see us.applying into SageMaker..It’s not just the launch.of the service..You can look at what we did.with SageMaker Studio last year..You can look at what.we’ve done this year,.at least what you’ve heard so far –.there’ll be more..And what we’ve done around data.preparation and Feature Stores.and the first CI/CD.in machine learning..We’re not close to being done.innovating here..The third ask that we often get.is customers say,.“I would like to be able to use.your machine learning,.but I don't want to have.to be responsible.for creating the model myself..I want to send you data..I want to use models that.you have built and trained on data.and get the answers back via APIs.”.And that’s what we think of.as this top layer.of the machine learning stack..We’ve a lot of services in this area..For people that want to look.at an object and say,.“What’s in this?” or a video and say,.“What’s in this?” we have an object -.a video recognition service -.called Rekognition..We have services that allow you.to go from text to speech,.to transcribe audio to text,.to translate that transcribed audio,.to do natural language processing.on all that translated,.transcribed audio.so you don't have to read it all.and know what’s happening..It lets you do OCR, but also OCR++.and being able to pull out data.from tables and formats.that don’t usually.come out in OCR..We give you the ability.to build chatbots with Lex..We give you the ability.to do internal enterprise.search with Kendra,.which we launched last year..There was a bunch of Amazon.capabilities that you asked us.to expose as machine.learning services which we have..So we have a personalization.machine learning service.and a forecasting service.and a fraud detection service,.and a code inspection service.called CodeGuru..And it’s interesting..As you start using.these models more and more,.there's that point in every company.where you think about,.“How much can I trust.these predictions.and these answers to use.in my applications?”.And there are certain use cases where.the ramifications are low enough.that you can take more chances..Oftentimes in applications that are.doing translation or transcription,.if you see good results early on,.you’re going to really trust it.as your major input..But there are other use cases where.the ramifications of getting it wrong.are significant enough where you’re.going to wait a little bit,.and these are services.like facial recognition.or forecasting your inventory.or the quality of your code..You’re going to look and make sure.that you like the predictions,.that they’re actually high quality,.and you’re going to use those inputs.really as one input.in several inputs of a decision..So it’s really machine.aided in that case..And what you’re going to wait for.is you’re going to wait.for the predictions.to become more.and more commonplace that you say,.“Yes, that’s the answer,”.and you want to see that.that provider is going to continue.to invest in that service.so you know it’s going to be robust.and you can really rely on it.and cut the cord.from what you were doing before..Let’s look at an example of this..Let’s look at CodeGuru..CodeGuru is a service.we launched last year,.which is a machine learning service.that lets you automate.code reviews to tell.if you have any kind of code.that you’ve written.that we think is going to lead.to a problem..And it also allows you to identify.your most expensive line of code..By the way, we have over.120,000 apps at Amazon.that are using that Profiler part.to find the most expensive.line of code,.which not only is helping them.find operational bottlenecks,.but also saving them.a significant amount of money..And so customers.who are trying to assess,.“Can I use….can I rely on CodeGuru as a major.input in how I think about my code?”.they say, “Well what’s.your commitment?.I understand you’ve got a profiler.or you have a code reviewer,.but how about more.programming languages?”.So the team launched Python.and a few other languages..Then they said, “Well how about.something around security?”.So the team launched.a security detector feature.that lets you know.whether or not you have code.that would lead.to some kind of security issue..And then people said,.“Well, that’s great..You have so much information about.the way that my application operates.and the way that all kinds.of applications operate on AWS..Why can’t you build a service.using machine learning.that allows us to predict.when there are going.to be operational problems?”.And so we went away.and thought about that..And I’m excited to launch.a new service today.called Amazon DevOps Guru,.which is a new service.that uses machine learning.to identify operational issues.long before they impact customers..[applause].And so DevOps Guru makes it.much easier for developers.to anticipate operational issues.before it bites them..And so using machine.learning informed.by years of Amazon and AWS.operational experience in code,.what we do in the service.is we identify potential issues.with missing or misconfigured alarms.or things that are….resources that are approaching.resource limits or big changes.that could cause outages.or under-provision capacity.or overutilization.of databases or memory leaks..When we find one of those that we.think could lead to a problem,.we will notify you either.by an SNS notification.or a CloudWatch event.or third-party tools like Slack..And we’ll also recommend.the remediation..And so when you look.at these top-level services.and you find them super useful,.as you go through this journey.that you will go through.as a company in determining.when you can trust them.as either the major.or a major.input in your applications,.you’re always going to want.to get.a lot of experience.running them at scale.and see the predictions.being more and more accurate,.and then also making sure that.the provider that you’re betting on.for that service is continuing.to invest in that service.and make it easier and easier.for you to use that as that input..And I think that’s what people.are starting to see with CodeGuru..The fourth ask that we get is from.customers who say the following:.“It is awesome that you have.so much machine.learning capability,.more than I could find elsewhere,.but I would like to somehow.benefit from machine learning.without having to even know.I’m doing machine learning.”.Perhaps my favorite.business school professor,.and also perhaps the best.innovation writer of our time,.was a fellow named Clay Christensen -.and Clay just passed away..But what Clay used to always.talk about was he used to always say.that customers hire products.and services for a job..And in this case what customers.are telling us is,.“I don’t want to hire you.for machine learning..I want to hire you.for a particular job.and if you use machine.learning to get it done,.great, that’s fine either way..But I want to hire you.to get that job done.”.And let me give you.a good example of that..If you look at business intelligence,.or BI, of course it started.with these kinds of older forms.of this, like Oracle OBIEE,.and then really over.the last few years.Tableau has done a great job.reinventing this space.and building beautiful.visualizations and dashboards..And we’re now at a stage.where BI is being reinvented again..And what people really want -.is they really want.their BI service to be serverless,.so they don’t have to manage.all that infrastructure..They want the ability to be able.to embed all their dashboards.and visualizations.everywhere easily, flexibly..And then increasingly,.they want to be able to use all kinds.of machine.learning and natural language..Really what they want.is natural language,.whether machine.learning fuels it or not,.that allow them to understand.the results more easily.than having to do all the work.to glean the insights themselves..And that’s why we built.Amazon QuickSight back in 2016..QuickSight is serverless,.it’s very scalable..It has, not just all the features.you need in a BI service,.but it also does embedding better.than you’ll find elsewhere..And then we’ve started.to invest in machine.learning that will allow you.to have experiences.that get things done.in a much more natural way..So for instance, we have this feature.in QuickSight called Autonarratives.and what it means is that when.you actually do a query.and you get the results,.you don’t have to actually.always do the work.to figure out what the results mean..We will provide natural language.what we think the key insight is..Now we do that through machine.learning, but nobody who uses.QuickSight has to know anything.about machine learning..And customers.really love that feature..And they’ve said, “I wish you could.use that type of capability.in other things.that we have to do in our BI usage.”.So for instance, I don’t like that.our users have to know.which databases.or data stores to get the data from,.or how the data’s structured.or how to ask a question.in a certain way or….I want them just to be able to type.into a search bar.a natural language question.and get answers..And this is a hard problem..A lot of companies have tried.to take a shot at solving this,.usually with natural language query,.and they just haven’t been able.to get good results..And it’s really hard.to build the right dataset.because there’s millions of questions.and pretty quickly you find.you didn’t have all the questions..You’ve got to go back to IT.and they have to build it.when they get a chance..It just hasn’t worked out until now..So I’m excited to announce.the launch of Amazon QuickSight Q,.which allows you to ask Q.any question in natural language.and get answers in seconds..[applause].And so the way Q works is you.just type in a question.in natural language in Q.in the search bar..You can ask a question like,.“Give me the trailing 12 month.sales of product X,” or,.“Compare product X sales to product.Y sales the last 12 weeks,”.and then you get.an answer in seconds..That’s all you have to do..You don’t have to know the tables,.you don’t have to know.the data stores..You don’t have to figure out how.to ask the question in the right way..You just ask it the way you speak..And Q will do auto filling.and spellchecking.so that it makes it even easier.to get those queries written..Q uses very sophisticated.deep learning,.natural language processing,.schema understanding, and semantic.parsing of SQL code generation..It is not simple..However, customers don’t need to know.anything about that machine learning..For them, the experience is: I type.in natural words.the way I would speak.and I get answers in seconds..By the way, we’ve trained those.models over millions of datapoints.and we’ve also trained them over all.these different vertical domains,.virtually every one you can imagine..This is going to completely.change the BI experience..So again, customers didn’t hire us.to do machine learning here..They hired us to make it easier.to ask the questions that.they want business intelligence on,.to get answers quickly,.whether we.use machine learning or not -.and that’s what we try.to do here with Q..So it’s pretty amazing how fast.machine learning.is continuing to evolve..And although we have over.100,000 customers.who are using our machine.learning services,.a lot more than you’ll find.anywhere else,.and a much broader array of machine.learning services.than you’ll find elsewhere,.we understand that it is still.very early days in machine learning.and we have a lot to invent.in all four of these areas.that we get asked regularly from,.from customers..Now we’ve talked about.the reinvention of compute.and the reinvention of data stores.and the reinvention.of machine learning,.and I think.that if you look more broadly,.if you’re wondering are.horizontal application areas.being reinvented or are vertical.industry segments being reinvented,.the answer is absolutely yes..And to kick us off on this section.and to share how they’re.reinventing broadcasting,.it’s my privilege.to bring to the stage.the CTO and President of Digital.for Fox, Paul Cheesbrough..[music playing].Through our partnership with AWS,.we’ve combined.best-of-breed infrastructure.with leading-edge.media operations..Production teams who are.more distributed than ever before.will be able to deliver.uncompressed video.through and from the cloud.with full redundancy,.which is an industry first..We’ll be able to produce.live events with less latency,.increased reliability, and.more efficiently than ever before..This collaboration has resulted.in the development.of entirely new services..Working with AWS, we’ve reinvented.how we produce and distribute content.to our consumers across all platforms.and devices using the cloud..We’ve transformed our.existing operation.but, more importantly, we’ve laid.the tracks for the future.in a way that will help us innovate.and adapt for many years to come..[applause].Thank you, Paul. We love.working with Paul and his team..They are unafraid of leaning.forward and inventing..They have built.a real reinvention culture,.and it’s remarkable.to see the way.that they are reinventing.broadcasting as we speak..Very honored.to be partnered with them..One of the questions.that we get asked.most from venture capital companies.and private equity companies is,.“Which areas do you think.are going to be reinvented?.Which application areas?.Which vertical business segments?.What’s going to get reinvented?”.And the answer we give.is often unsatisfying,.because in our opinion.the answer is, “All of them”..And that’s true in every single area..I think you’re seeing it happen today.and those that you’re not.seeing it happen.in yet, you will see it.in the next few years..And so let’s start with.horizontal application areas..And let’s start with an example..Let’s look at call centers..So if you look.at call center solutions,.the last 20-30 years people have not.liked these solutions very much..They’ve been hard to set up,.they required expensive consultants.and lots of hours,.if you need to change anything.you require a complicated code,.they were hard to scale up and down,.they are expensive,.and they are missing the two.most transformational technology.advances of the last 15 years,.which is cloud and machine learning..Now, we learned.this first-hand at Amazon,.where we had to build.our own call center solution.to handle our retail business,.and we had a lot of AWS.customers who said,.“Why don’t you just generalize that.and expose it to us as a service?”.And that’s why we built.Amazon Connect,.which is our call center service,.which we built in 2017..And people love this service..It’s one of the fastest growing.services in the history of AWS,.and the reason they love it is.that it’s easy to set up,.it takes minutes instead.of outside consultants.and lots of money,.it’s easy to scale up or down agents,.it’s much more cost effective,.because you only pay.for what you consume,.and you are only paying for.when your agents.are interacting with customers..There is no infrastructure.for you to deploy,.and then it’s built right from.the get-go on top of the cloud.and with a lot of machine.learning and AI embedded,.so you can do things.like chat bots.and IVR and transcription of audio.and sentiment analysis.without having to worry.about the machine learning at all..And Connect is growing.really rapidly..You see thousands and thousands.of customers are using it,.companies like Capital One.and Intuit, John Hancock,.and GE and Petco.and Barclays and State Farm..And what’s also interesting is,.during the pandemic.over 5,000 new companies,.new Connect customers,.have started using the service.where they spun up call centers.remotely to help them deal.with the fact that all their customer.service agents were now remote..And so this is another.one of those services.that’s growing.unbelievably quickly,.and people are excited about.and giving us all sorts of input.on what else they would like.to see us solve with the service..And so I am going to share a few.of their asks across three categories.and five different feature ideas..And the first is really around.how can we make it easier.for our customer service agents.to have the right information.about products and customers.right in the moment, in real-time,.when they are dealing with customers?.And the problem is.an interesting challenge..When customers call in about.something, a service or product,.you have all this information.that lives.in all these different databases,.some on-premises and first-party.and some of which are third-party,.which you have your agents.try to access and toggle.between different databases.and find the right information,.which is slow.or they don’t do it all..The same thing happens.when you have customers.who interact with you.across lots of different silos.in your business,.and different databases,.either first-party or third-party..And so customers said,.“Can you make it much easier for us.to have the right product information.and customer information.for our agents.to have that fast.holistic service?”.So I have two new features.to announce here for Connect.to help with this problem..The first is Amazon Connect Wisdom,.which is a new capability.that uses machine.learning to deliver agents.the product and services information.they need to solve issues.in real-time..[applause].So Wisdom has all these.built-in connectors.to relevant knowledge.repositories,.either your own.or there’s some third-party ones..We’ll start with having connectors.to Salesforce and ServiceNow,.but there will be more coming..And then what Wisdom lets you do.is that, as a call is happening,.Wisdom is using machine.learning to listen.to that call transcription.from Contact Lens.and then detect issues.that are happening.and put the right information.in front of agents..So, for instance, if you have.a call and Wisdom hears.‘arrived broken’, it will search.all the relevant data repositories.for the information.that you need around what to do.when you have a product.that arrived broken..So the agents have it right there.in front of them automatically,.right in their console..That’s a game changer..And if it turns out that you don’t.get the right recommendation,.or you want to get.more information,.agents can also just type.in natural language questions.into the Wisdom search bar.which pulls all the information.across all those databases..So this totally changes.what information.you have available to you, product.and service-wise, for your agents..And then, for trying.to figure out how to.provide a more holistic.customer experience,.and pull together all.the customer profile information,.I am excited to share.a second feature,.which is the launch of.Amazon Connect Customer Profiles,.which gives agents a unified.profile of each customer.to provide a more personalized.service during a call..[applause].So here’s how.Customer Profiles works..You will point Customers Profiles.at your internal databases,.where again, a third-party database.will launch Customers Profiles.with your being able to access.your information in Salesforce,.in Zendesk, ServiceNow, and Marketo..And then, when the call comes,.Customer Profiles connects.that phone number or contact ID.with a customer ID.that’s used consistently across.all those data stores..And so, Customer Profiles knows.how to ingest and normalize.that data across customer.contact history,.or e-commerce status,.or order management,.or marketing communications.you’ve sent,.or sales or CRM information..And they know how to display it.in a concerted organized way.so all that appears in front.of an agent’s screen..And so, take a simple example..Let’s say you are a hotel company.that’s using Connect,.and let’s say you have.a customer call.in who’s unhappy about a stay.they had the prior night..You might handle.that call differently.if you also knew.that that same customer.called you earlier in the week.for their company to ask for a quote.to do a five-day off-site.at that hotel..You might have.a different conversation..Today, agents don’t have.access to that information..With Customer Profiles,.all that information is there.for agents to use at the same time,.and changes how they can have.holistic customer interactions..Very, very useful..The next category of asks.we get around Connect is,.“How can I make it easier that.when I have a customer contact.or call that’s going off the rails,.how can I intercept that.before the call is done.and before I have a bad experience.for the brand?”.And you may remember that last year.we launched a service.in Connect called Contact Lens,.which is a call analytics service..And what Contact Lens does.is it stores all your calls in S3,.and then it transcribes it to text,.and then it indexes and tags.that information so it.makes it easier.to search and find,.and then it gives people,.agents or managers,.the ability to have full-text.transcriptions of those calls,.to be able to understand.when there was a negative sentiment,.or when there were long.lapses in conversation.or people were raising their voice.or talking over each other..And so it totally changes.your knowledge and information.about how these contacts went..And customers.have loved Contact Lens,.and they have used it.very pervasively..But a number of our customers.and the supervisors.of these customers have said,.“Look, I wish I had the chance.to know in real-time.when a call was going awry,.because I could either coach.that agent in real-time,.or have it move to me.to avoid any brand harm.”.And so the team has worked.on this for the last year,.and I am excited to announce.the launch of Real-Time Contact Lens,.which identifies issues.in real time.to impact customer interactions.during the call itself..[applause].And so Real-Time Contact Lens.uses more developed machine.learning to do.natural language processing.on audio calls.in real time,.instead of waiting a few minutes.after the call ends.and the data and the speech.is transcribed to text..They do it in real time..This is not easy to do,.which is why it took.the team a year to do it..And it’s a good example.of not only being able to leverage.AWS’s machine learning expertise,.but again, being able to use machine.learning where it’s working for you.under the covers,.where you don’t actually.have to worry about it at all..The job you’re trying to get done.is you want to be able to have.an impact on calls in real time..And so what managers do who are using.Real-Time Contact Lens,.is they specify terms.where they want to be alerted..So they may say,.“If, in the transcript I hear.‘bad experience’ or ‘unhappy’.or ‘never using you again’…”.When that criteria is met,.it sends an alert in the Contact Lens.dashboard to supervisors.and they can either choose.to coach agents real-time,.or have the call.transferred to them..And if the call.is transferred to them,.they also get the real-time.transcription of that call.right in front of them,.so they don’t have to ask customers.all the same questions,.which is frustrating.for those customers..So again,.a really big deal for people.who are doing customer service..If you can know when a call.is going awry in real time,.and help that customer.have a better experience.and not do harm to your brand,.that’s a big deal..The third category that we get people.asking us for help in is,.“How can I further optimize.my agent’s time in various areas?”.And the two areas that they asked are,.first, they said,.“About half of my time for agents.is spent on doing tasks,.tasks outside of calls”..You might have to file.an insurance claim for a customer..You may need to contact.the customer for a status change..Half of their time.is spent on these tasks..These tasks live everywhere..They are not organized.in any one central place..It’s hard to prioritize them..They are kept on pieces of paper,.that’s why oftentimes.people lose them..This is a big problem..The other thing that people ask.to optimize agent’s time is they say,.“At the beginning of a lot of these.calls, our agents have to spend time.manually authenticating customers and.asking all these manual questions,.and it wastes a lot of time.for agents,.it wastes a lot of time.for customers..Can you help with that?”.And so we have two solutions here.to help you with these problems..The first is the launch.of Amazon Connect Tasks,.which automates, tracks, and manages.tasks for contact center agents..[applause].And so Tasks makes it much,.much easier.to be able to manage tasks..Now you have one central place where.an agent to see all their tasks,.managers and supervisors.get that same view,.managers can easily assign.the tasks there,.they can choose to prioritize.and where they want what agent.to get each task done..They can actually decide.to assign agents’ tasks.based on how busy they are.and whether they are.on the phone or not..And then agents have everything.right in front of them..And the nice thing also is.that both agents and supervisors.can automate.a number of elements of tasks.to make it even faster for them.to get these things done..So this is very useful..This is going to change.the efficiency of agents.when they are not on calls..People are very excited about this..And then, for the call.authentication issue,.I am excited to share with you.the launch of Amazon Connect Voice ID,.which is real-time caller.authentication using machine.learning powered voice analysis..[applause].And so the way that voice ID works.is that if you’re using it,.you’ll ask whether customers.want to opt into it,.so they can have a better chance.of avoiding fraud.and also.so they can save time in their calls..They opt in,.and they say a few sentences.and we build effectively a voice.print for each customer..And then, when a caller calls in,.we will just allow them.to start talking..They’ll start talking about.what they’re calling in for.and what their problem is,.and within a few seconds Voice ID.will return to you.who they think that customer is,.and with a confidence level.of that prediction..Each company will set.their own threshold,.but if it meets that threshold,.you just continue having that call,.you know there is no chance of fraud,.you don’t waste all that time.on the manual questions..And if it’s under that threshold.that the company sets,.then either you can choose.to do manual verification,.or transfer that call to a fraud.specialist, but again,.it totally changes the efficiency.and the productivity both for agents.as well as for customers.who are calling in..And so if you think about it,.call centers.and call center solutions existed.in a certain way for a long time..And even though customers.didn’t like them,.what happened was they left.this area ripe for reinvention..And when you have an opportunity.to reimagine an area.in an experience like a call center,.and you find a way to make it easy.to get started and easy to scale,.and much more cost effective,.and you build capabilities.that put all the relevant information.about products.and customers.in front of agents.so they can handle.those in real time,.and when you allow agents.to be more productive.with their tasks outside of calls,.and you allow them to actually.start calls much quicker,.and then you allow them to use.machine learning under the covers.to do things like have the ability.to impact calls in real time.before there is any harm done.to a brand -.when you do all.those types of things,.you can completely transform.any horizontal application space.as we have with Connect..Every single one of these.horizontal application spaces.has the ability.to be reinvented.and will be reinvented.in the next number of years..How about vertical business segments?.Which of these are being reinvented?.And the answer is, all of them..And I am going to give you.a few examples..If you look in the auto space..This may be the space.that has the most elements.of reinvention.happening simultaneously,.but you have.the electrification of cars,.which is partially driven.by the environment and partially driven.by companies that came up.right from the get-go,.electric only companies like what.Rivian has done on top of AWS..You have companies who are working.and building autonomous vehicles..We do a lot of work.with BMW and Lyft on these,.but virtually every company.is working on this..The car gives you a chance to have.all these new connected experiences.that weren’t really thought.of many years ago..We’ve been working with companies.like Toyota on this,.but even today you may.or may not have seen the announcement.we just made.with BlackBerry,.where they have built a new.intelligent vehicle data platform.that they call IVY,.on top of AWS,.where they basically.took their QNX software.that does sensor management.and that runs on.175 million vehicles on the road,.and then what they’ve done.is they’ve helped automakers.better access,.process, analyze, and share data.from those connected vehicle sensors.to build brand new experiences.for car owners.or drivers or passengers..So auto is transforming.in a rapid way..The same thing with healthcare..Healthcare companies will tell you.that the Holy Grail for them.is to be able to take all these.disparate pieces of information.that live in all.these different formats.so they have.a 360-degree view of customers..And they are not there yet,.although we’ll have.more to say on this.and to help with this.in the next couple of weeks..But they’re not there yet..But they are making progress..And they’re using machine.learning to solve problems.that they couldn’t solve before..So a good example is look at what.Moderna has done in the last year.or so,.the last nine months..They’ve built an entire digital.manufacturing suite on top of AWS.to sequence their most recent.COVID-19 candidate.that they just submitted.and it has 94% effectiveness..And they did it on AWS.in 42 days,.instead of the typical.20 months it takes..Just think about that..Think about what it changes.with regard.to what’s possible in healthcare.if you’re able to take actions.that are so momentous like that much.more quickly and much more easily..You see the same type of invention.happening in media and entertainment..I think most people here know.that the way.that we have started streaming.and allowing consumers.to use video content.has radically changed.in the last five years,.and if you look at all.the major streaming services,.Netflix and Disney+ and Hulu.and Prime Video,.they all use AWS to do that,.but you are also seeing.really audacious new inventions.like what Paul was talking about.with Fox and ViacomCBS.are trying the same type of thing,.which is completely reinventing.how they do broadcast.and the ease and speed.with which they can spin up.brand new channels for people..And then you see.the same thing.happening in industrial.manufacturing,.where they are using edge.and the data.to rethink all their design.processes on the production line,.thinking about how to change.their supply chains,.how to change their products..And a company that’s.a great example of this,.which is totally reinventing.themselves on top of AWS,.is my next guest..It’s a privilege to welcome.the President and CEO of Carrier,.Dave Gitlin..[applause].Thank you, Andy..I am honored to be with you today..Like Amazon, Carrier is a company.founded on innovation..Willis Carrier invented.modern air conditioning.at the age of 25 back in 1902,.and it changed the way we live..Innovation is in our DNA.and it is embedded in what we call.‘the Carrier way’..With a laser focus on customers,.Innovation, and agility,.we introduce more than.100 new products a year..And that kind of relentless customer.focus and quick execution.is what Amazon calls Day One..We literally just had our Day One.when we became.a public company on April 3rd,.following our spin.from United Technologies..And it has been so energizing..We think of ourselves.as a 100-year old start-up..And our focus is clear..To be the world leader in healthy,.safe, and sustainable building.and cold chain solutions..On the building side,.COVID has shined a light.on the criticality.of health and safety,.and we are leading the way.to ensure healthy buildings.and safe indoor air environments..But today I am going to focus.on our other ecosystem,.and that’s the cold chain and.our transformative alliance with AWS..So let me start.with a problem statement..The safe distribution of many foods.and pharmaceuticals requires.uninterrupted cooling from inception.to destination..Maintaining precise.cooling conditions.is more difficult.than it sounds,.because foods and pharmaceuticals.can travel thousands of miles.through a complex.refrigerated distribution network.called the cold chain..So consider a banana..Bananas are the most.consumed food on earth.and are extremely.temperature sensitive..Once harvested,.their clock starts ticking..Within hours,.bananas must be cooled.and maintained between.56 degrees and 58 degrees Fahrenheit,.with relative humidity.between 90 and 95%..Even a one degree.temperature variation.during that refrigerated journey.can cause damage.which doesn’t show up.until arrival at the supermarket..So consider the two.to three-week journey of a banana.from, say, Ecuador.to the United States..It starts at the farm..There, bananas are packed.into a non-cooled cardboard box..They are then put into a series.of refrigerated environments.starting with a cold room.on the farm to a truck,.to an ocean container,.shipped over the ocean,.then onto a trailer.to a distribution center,.onto another truck.to a ripening facility,.to yet another truck.to the grocery store..Each of these hand-offs from one.refrigerated environment to another,.occurs at outside temperature,.and there is no margin for error..And today, there is.no central repository.for the temperature data.for the entire journey..So now it’s not surprising.to learn.that one third of the world’s food.produced every day is never consumed,.due to waste, loss, or damage..Imagine that..Food waste costs the global economy.nearly $1 trillion..But here’s what’s.even more tragic..One in nine people.go to bed hungry every night..If we could eliminate all of.the cold chain challenges,.we’d preserve enough food.to feed 950 million people..That’s more than all of the hungry.people in the world today..Think about the impact on the planet..Consider all of the resources.and resulting emissions.that go into producing food.that never gets consumed..If food waste were a country,.it would be the third largest.emitter of greenhouse gases..It’s tragic on a human level,.an environmental level,.and a financial level..And the same problem exists.with pharmaceuticals..They must be kept cold.within very strict standards.from the time the active.pharmaceutical ingredient.is first manufactured,.through a series.of hand-offs spanning ocean,.air, warehouses, trailers,.vans, freezers,.and then temperature-sensitive.medications must then be administered.within hours.of hitting room temperature..Today, we lose $35 billion.in biopharma each year.from temperature-controlled.logistics failures..It’s a challenging problem.in normal times,.but an unprecedented challenge.with the COVID vaccine..So now let’s talk solutions..We at Carrier are a global leader.in providing equipment.that cools containers,.trucks, trailers, temporary.remote storage, and cabinets..We also provide sensors.that track temperature,.location, and cargo condition..AWS has the world’s most widely.adopted cloud platform.with the broadest.and deepest set of services..Together we are going to transform.how perishable food.and pharmaceuticals are protected.through a new offering.that we’ve developed, called Lynx..You can think of Lynx.as an end-to-end digital.connective platform.for the cold chain..We will use AWS’s IoT,.Analytics, and ML services.to help customers.reduce food and medicine loss,.optimize supply chain logistics,.and enhance.environmental sustainability..Where data was previously siloed,.we’ll now be able.to aggregate temperature data.across the products’.entire journey onto a data lake..Say, from the time the banana.is first cooled on the farm.to when it arrives.in the supermarket..And we can use AWS.ML capabilities to provide faster,.smarter, actionable intelligence.to better manage the cold chain.and prevent breakpoints..With AWS ML.services, for example,.we can learn that truck doors.should be not be left open longer.than half an hour.when loading or unloading cargo..With AWS ML services.we can determine if a container.has to be pre-cooled to 56 degrees.and then use position location.to initiate the pre-cooling process..We can also use weather data.to detect.and avoid.transportation delays,.and then use updated arrival times.to alert the supermarket to a delay,.so they can then adjust.their produce displays..The data can also be used.for equipment health,.to remotely diagnose.a compressor issue.while the container is at sea,.or better yet, use AWS IoT.and analytics to help anticipate.when a unit will fail,.so we can then perform.preventative maintenance..And we can also optimize logistics.by combining truck.positioning data with traffic.and weather to help.a distribution company.improve fuel consumption.and fleet up-time..All of this, helping food.and pharmaceuticals.get to where they are needed.when they are needed.and in the conditions.that they are needed..So exciting times for us.here at Carrier..With AWS we will be addressing.profound issues like hunger,.climate change,.and safe vaccine distribution,.and we could not be more excited.to be on this journey.with our friends at AWS..Thank you..[applause].Thank you, Dave..It is really impressive.what Carrier is doing,.and really important,.particularly at this time..And as we said earlier,.the keys to reinventing.is a combination of building.the right reinvention culture.and then knowing what technology.is available to you.to make that reinvention.change and using it..And that’s exactly.what Carrier is leveraging..It’s very important.and very impressive..So manufacturing and industrial.companies.are a group of customers.who have said to us,.“We know that we could change.our customer experience.and how we operate.our plants.so significantly using.machine learning,.but we just don’t often.have the equipment.or the talent.to make that happen,.and I really wish.AWS would help us.”.And so let me give you.a couple of examples..If you look at machine data,.there are a lot of industrial.companies.who know that if they could do.predictive maintenance better,.they could save a lot.of money and time..And what manufacturing.companies will tell you.is that it’s always.much less expensive.to fix something.before it breaks,.not to mention saving the money.of the downtime in the plant..But it’s actually.not that easy to tell..Most people at a plant.will tell you that you can hear it.or you can feel it through.the vibration before you can see it..But a lot of companies.either don’t have sensors,.or they are not modern.and powerful sensors,.or not consistent.and they don’t know.how to take that data.from the sensors.and send it to the cloud,.and they don’t know how.to build machine learning models..And our manufacturing companies.that we work with have said,.“Could you just solve this?.Can you build.an end-to-end solution?”.So I am excited to announce today.the launch of Amazon Monitron,.which is an end-to-end solution.for equipment monitoring..[applause].And so Monitron gives.customers sensors,.a gateway device.to send the data to AWS..We build custom machine.learning models for you.in a mobile app with a UI.so you can tell what’s happening..And all our customers do is you mount.the sensors to your equipment,.you start sending the data.through the gateway device,.and then, as we take the data in,.we build the machine learning model.that looks at what ‘normal’ looks.like for you on sound or vibration,.and then as you continue.to stream that data to us,.we will use the model to show you.where there are anomalies.and send that back to you.in the mobile app,.so you can tell where you might.need to do predictive maintenance..That’s a big deal..That makes it much,.much easier for companies to do..Now, there are other companies.who say,.“Look, I have modern sensors.that I’m fine with..I’m also okay taking the data.from those sensors.and sending it to AWS,.but I don’t want to build.the machine learning models..I just want to send you.the data, use your models,.have the predictions come back.to me through the API.”.That third layer.of that machine learning stack..And so we have something.for this group of customers.as well to announce today,.which is the launch of.Amazon Lookout for Equipment,.which shows anomaly detection.for industrial machinery..[applause].And so with Lookout for Equipment.you just send the data to S3,.or we have a service.called IoT SiteWise.that lets you send.your machine data.in a structured way.for your analytics..You send the data to AWS,.we will assess sound,.vibration, temperature,.and we’ll again build a model.of what normal looks like,.and as we see anomalies.we’ll send them to you via the API,.so that you can do.predictive maintenance..These are game changers.for industrial companies.that want to be doing predictive.maintenance and saving money..And the second problem they asked.to help with was computer vision..And if you think about it,.there are a lot of split decisions.that you’ve got to make.in facilities,.on your production lines,.or even in interactions.between people.when you’re trying to be.socially distanced,.where you just.don’t have the time.to send that information to the cloud.and get an answer back..You need to make.that decision in real time..So what these.industrial companies need,.and often try to employ.in some fashion, are cameras -.these smart cameras that allow them.to do streaming video..But the problem is, most of.the smart cameras out there today.are just not powerful enough.to run sophisticated.computer vision models at the edge..And most companies you talk to,.they don’t want to rip out all their.cameras that they have installed,.but they know they need.to give help to those cameras.to be able to do computer vision.in a sophisticated way..They asked us if we would.try to help with that..And so I am excited.to announce today.the launch of the AWS.Panorama Appliance,.which is a new hardware appliance.that allows organizations.to add computer vision.to existing on-premises.smart cameras..[applause].So here’s how it works..You simply plug.in the Panorama Appliance.and connect it.to your network,.and Panorama starts to recognize.and pick up video streams.from your other cameras.in the facility..The Panorama Appliance.can accept streams of up.to 20 concurrent streams.and operate on those..If you need to have.more concurrently,.you can buy more.Panorama Appliances..And then we have pre-built.models inside Panorama.that do computer vision for you.and that we’ve optimized by industry..So we’ve got them.in manufacturing,.in construction, retail,.and a host of others..You can, of course, choose not.to use the pre-built models.and build your own in SageMaker,.and then just deploy those.to Panorama..And then Panorama.also integrates seamlessly.with the rest of the AWS IoT.and machine learning services,.where, if you actually.want to send that data,.not for real-time actions,.but to do large-scale analytics.on what’s happening in the plant,.you can send them to us.through Panorama.and you can use it.in the rest of your AWS Regions..Now, this is pretty exciting,.and people are pretty excited.about the possibility.of having real computer vision there,.but they also have told us that,.“Look, we’re going to buy the next.generation of smart cameras,.and those smart camera.manufacturers have told us.we want to actually.embed something.that allows us to run more powerful.computer vision models in there,”.so we are also providing.a brand new AWS Panorama SDK.which enables hardware vendors.to build new cameras.that run more meaningful computer.vision models at the edge..[applause].And so what this will do is,.if you are a company.that runs cameras,.and you are building.the next generation of cameras,.you will be able to use this SDK.and the APIs associated with it..We’ve done all this work.to optimize the parameters.around memory and latency.so you can actually, in the camera,.fit more powerful models.in a more constrained space,.and it’s going to change.what’s available for companies.as they’re building.smart cameras moving forward..If you’re an industrial company,.and you’ve built a culture.that’s able to reinvent,.if you use these tools.I just talked about,.along with a host of others.that AWS provides,.you can totally reinvent.what you are doing.in your industrial.manufacturing company,.much like Carrier has..And the reality is that this is true.and available to every single company.in every vertical business segment..That reinvention is there for you..If you’re ready, we have a lot for you,.with a lot more coming..So the final area of reinvention.that I’m going to talk about today.is really around.hybrid infrastructure,.and I am going to yield the floor.to the Head of Infrastructure.at Riot Games, Zach Blitz..[music playing].With Outposts, AWS.gave us a unique solution.to ensure a level playing.field for our players,.and streamlined our deployments.using the same tools and APIs on-.premises and in the cloud..We rolled out Valorant fast.and we are continuing.to reinvent.how we design and.deploy our games.to provide our players with the best.possible game experience..We shipped AWS.Outposts to new colos quickly,.enabling a rapid deployment.of game servers,.standardized on a single build,.test, and production pipeline..And best of all, using AWS.Outposts, we reduced latency.for players by 10-20 milliseconds,.minimizing peeker’s advantage.and creating a level playing.field for all players..[applause].Thank you, Zach..I appreciate it, Zach..It’s very impressive.to continue to see the way.that Riot is innovating.for their players,.and it’s also impressive to see.how they’re using AWS in Regions.as well as on-premises.in a seamless and consistent way..When you think about the term.‘hybrid’.and ‘hybrid infrastructure’,.I think a lot of people believe.that this term.and these solutions are pretty set..But in our very strong opinion,.both the definition of the term.and the solutions themselves,.are innovating and being.reinvented really quickly..When people ask,.“What’s hybrid?”, you know,.typically, people view it.as a mix of modes..And people define it early on.as a combination of cloud.alongside on-premises data centers..And one of the reasons.it was defined that way.is the people.who popularized this term.were on on-premises.infrastructure product providers,.and they wanted to ride along.with the momentum of the cloud..So that’s how.the definition started off,.which was cloud.and on-premises data centers..And it led to all.this breathless debate.about whether this was going.to turn out to be a binary situation..Would you only use the cloud.or only use on-premises?.And we probably contributed.a little bit to that confusion,.because we stated.our then very strong belief.and now even stronger belief,.that the vast majority of companies,.in the fullness of time,.will not have their own data centers..And those that do will have.much smaller footprints..But we always thought.that was going to happen.in the fullness of time,.not this year..And we knew that it would.take several years.and so we just spent.a lot of our time.in the early years of AWS.on the cloud-specific pieces,.and then building sensible bridges.back to on-premises data centers..And these are things.like Virtual Private Clouds,.or VPCs, or Direct Connect,.or Storage Gateway..But in the meantime,.you had a number of companies.who tried to jump on.owning what hybrid was,.and built all of these.hugely-hyped capabilities.that were supposed to be.hybrid capabilities.that never lived up to the hype,.and really never got any traction..And so we were watching.this happening,.and we went back to first principles.and we asked ourselves,.“So, wait a second..What really is hybrid?”.So it’s cloud and on-premises..What does on-premises mean?.Is it just on-premises.data centers?.What about a restaurant?.Is that on-premises?.What about an agricultural field?.Is that on-premises?.If those are on-premises,.they have very different requirements.than on-premises data centers,.and so we think.of hybrid infrastructure.as including the cloud,.along with various other edge nodes,.on-premises data centers.being one of them..But there are several of them..And the way that customers.have told us.they want to consume our hybrid.offering is with the same APIs,.the same control plane,.the same tools,.and the same hardware that they are.used to using in AWS Regions..Effectively, they want us.to distribute AWS.to these various edge nodes..So we reimagined.for ourselves what hybrid was,.and we started to build solutions.that picked off.the biggest use cases,.but in a way that worked for.customers both short and long-term..And so we started with,.customers said,.“Look, I want to be able.to use the same tools.I’ve used to manage.my infrastructure on-premises.for the last number of years..I want to use that in the cloud..I want to use it to manage.my cloud deployment.”.Most of the world, at this point,.has virtualized on top of VMware..So we started working.with Pat Gelsinger.and the VMware team.on how to build a new offering,.which is called.VMware Cloud on AWS,.that allows customers.to use those VMware tools.they have been using.for many years.to manage their.on-premises infrastructure,.and manage their infrastructure.on AWS..And this is a very.unusual collaboration..There’s no other managed service.that VMware runs.with another cloud provider..There is none that have.the functionality.and capability of this.VMware Cloud on AWS..It’s not just that.both VMware and AWS.have its engineering teams.and its product teams.closely tied at the hip,.but also our field teams.and our partner teams work together.with customers..It’s a very unusual partnership..And it’s gaining a lot of momentum.and a lot of steam..And you can see there.are a lot of customers,.whether you are talking about.S&P Global or PennyMac,.or Johnson & Johnson or Phillips.or Palantir, Scottish Government,.Lots of customers are using.VMware Cloud on AWS..You also see the growth,.almost double the amount.of nodes year over year..IDC just had a report.that showed over five years.you get a 500%.return on investment..There’s a lot of momentum.in VMware Cloud on AWS.and it’s really, really handy,.as you’re moving from on-premises.infrastructure to the cloud..Then a lot of our customers said,.“Well, that is awesome,.that is very useful,.but what about when I need.to keep workloads on-premises.for the foreseeable future?.Maybe they need to be close.to a factory or something,.that lives near my data centers,.but I want to use.AWS there right on-premises.”.So there are a number of companies.that have tried.to solve this solution,.none of which have gotten.any traction..And we tried to go to school.on what wasn’t working..And what customers.hated about those solutions.was that it didn’t have the same.APIs or tools.or control plane or hardware..They were totally different..There was too much work,.and they weren’t doing it.because it was too much friction..And so we changed how we thought.about what people wanted there,.and we changed our mindset.to realize what they really wanted.was they wanted us to distribute AWS.to the on-premises locations and nodes..And so that’s why we built Outposts,.which we announced two years ago.and launched last year,.which effectively rolls.in racks of AWS services..You’ve got compute, storage,.database and analytics,.and soon machine learning..It’s fully managed..We deliver it, we install it,.we’ll do all the maintenance..And it comes with the same APIs,.the same control plane,.the same tools, and the same hardware..People are very excited.about Outposts..In a short amount of time with.thousands of customers using them,.these are customers like Philips.and Volkswagen and Erikson and Cisco,.Lockheed Martin, T-Systems,.and Toyota..Just loads of companies.who are using these services,.and using Outposts.and very excited about it..And so customers said,.“While we love Outposts,.and it really worked well on.on-premises data centers,.but they are big racks..What if I want to use Outposts.in a much smaller space.where I can’t afford to have racks?....I don’t have the space..I need servers.”.And so I am excited to announce today.two new formats of Outposts,.smaller Outposts.that let you run AWS infrastructure.in locations with less space..[applause].And so instead of these big rack.solutions,.these are server solutions.for Outposts..So the first size.is what we call 1U size,.which is 1¾ inches tall,.the size of a pizza box..It’s a 40th of the size.of the bigger rack.Outposts that we launched a year ago..And then we have a 2U size.which is 3½ inches tall,.which is almost like two pizza boxes.stacked, and these two smaller.Outposts formats.have the same functionality as.Outposts, just for a smaller space..And now it means.that restaurants or hospitals,.or retail stores or factories can use.Outposts to have AWS.distributed to that edge..Then customers said,.“Okay, that’s really cool. I can have.Outposts on-premises data centers.in a much smaller space..How about the use case where I need.AWS distributed.in major metropolitan areas.where it may not be cost-effective.either for you at AWS.or me, the customer,.to have a data center there,.but I’d be willing to pay.a little extra.in exchange for my most.demanding low-latency applications.being able to reside.in major metropolitan areas?”.And that’s why we built and announced.last year AWS Local Zones..We started with the first one in.Los Angeles,.aimed at the film makers and graphics.renderers and gaming companies..And I am excited to announce today.we have three new local zones.that are launching today..In Boston, in Houston, and in Miami,.and then 12 more in the United States.in 2021, in Atlanta, Chicago,.Dallas, Denver, Kansas City,.Las Vegas, Minneapolis, New York,.Philadelphia, Phoenix,.Portland, and Seattle..[applause].I don’t know if you’re applauding.because you’re excited.about the Local Zones,.or that I remembered all 12 of those..But we’re excited about it.and we think it’s going.to help you deploy AWS.as metropolitan city edges..“What about when I actually need.AWS distributed to the edge.where there’s no connectivity,.or where the terrain is so rugged.that I need something that can.be banged around a little bit?”.This might be in an.agricultural field or an oil field,.or a military battlefield..And for this, we built.our Snow Family of products..So, Snowball Edge, and Snowcone,.which are different sizes,.but both are hardware appliances.that you can bring.to that disconnected edge..It stores data that’s running off.the different assets.that you have with the edge..It has Compute on it,.so you can do some analytics.and processing on it,.and then, if at any point you want.to actually detach that appliance.and send it back to us to have it.adjusted in our data centers.so that you can do.large-scale analytics,.you can do that as well..“How about distributing AWS.if I want to build.mobile applications.and I want to take advantage of 5G?”.And people are very excited about 5G,.because the latency.and the power of it.give you a chance to build sub-10.millisecond latency applications.that can do things.like smart manufacturing.or autonomous vehicles.or various things in games..But any application that wants.to do anything interesting.is going to need compute,.it’s going to need storage,.it’s going to need infrastructure..And the problem is,.for mobile applications.to leverage that infrastructure.which is typically AWS,.they have to go from the device.to the mobile network,.to the local aggregation location,.to the regional aggregation location,.to the internet, to AWS and back..And that’s not 10 milliseconds,.that’s seconds..And so customers wanted.a way to change that,.and that’s why.we announced last year.the launch of what we built,.called AWS.Wavelength, which extends AWS.infrastructure to the 5G edge,.so now you only have to go.from the device.to the natural 5G aggregation site,.and AWS has racks of Outposts.right there for you.to do your infrastructure..We launched this.last year with Verizon,.who’s been an amazing partner..We already have eight US cities.that we’ve launched,.with more coming in the coming weeks..We are launching with KDDI in Tokyo,.and SK Telecom in South Korea.in the next few weeks,.and then with Vodafone.in London in early 2021..And one of the problems.that we also thought about.and saw that the customers.asked about was they said,.“Look, there are all these.different telecom providers..They all have.different semantics..I don’t want to have.to learn all those..Can you build an abstraction.so I’m just writing.to a Wavelength zone,.and then you do all.the normalizing under the covers?”,.which is what we’ve done..So there are a lot of ways.of being able.to bring the AWS experience.to customers wherever they are..And so when you go back.to asking “What is hybrid?”.It’s not just cloud.and on-premises data centers..It’s cloud along.with various edge nodes,.on-premises data centers being.one of them, with several others..And we think most of this computing.will end up in the cloud over time,.which will be like the hub.just given the cost and the agility.and functionality and productivity.advantages for builders..But several other workloads.will reside where it makes most sense..You will have on-premises.data centers.when you’re doing this transition.from on-premises to the cloud,.where they need to be close.to something.that lives.near an on-premises data center..You’ll have them in various smaller.venues where you want them,.in a restaurant or in a hospital,.or in a factory..You’ll have them.in major metropolitan areas.where you have your most.demanding low-latency workloads,.where you are willing to pay.a little bit extra.to have that low latency..You’ll be able to have them.in the disconnected edge,.and you’ll be able to have AWS.as well when you’re building.5G mobile applications.that need to sit at a 5G mobile edge..And people will want this hybrid.experience to be delivered by AWS.by distributing AWS to these.edge nodes with the same APIs,.the same control plane,.the same tools,.and the same hardware.they get in AWS Regions..That’s where we believe.hybrid is heading,.and how we’re trying.to enable it..So I’m going to close.with a song lyric,.and because of COVID and the way.we did this virtually,.we didn’t have the band this year,.but I’m going to use a song lyric.to try and bring us home..And the lyric says,.“I wish I could Google my ending..Someone give me reassurance,.answers, anything will do.”.And this is from one of my.very favorite song writers,.and what she was talking about.was really the uncertainty.of being a young adult,.and what’s going to happen..But I think that same message.is really applicable to companies,.and what’s going to happen.in the future..If you’re a missionary.and you’re focused.on building a lasting company versus.making a quick buck.and being a mercenary,.you know how hard it is.to build a sustainable business..So many things can derail you..New technology, new competitors,.losing leaders,.losing key contributors,.regulation, pandemics..There are a lot of things..And like for a young person.wondering what the future.holds for them,.the same is true for companies..It’s daunting..And I think.the same counsel applies..You can’t control for every change.and every development,.but you can build the capacity.to get to the truth,.to make changes.when they’re needed,.to have people around you.who want to help you change.and can help you make the change,.stay focused on what matters most,.to move fast when speed is required,.which is more often than you realize,.and then to be aware.of what’s available to you.and what’s changing around you,.so you can reinvent who you are.and what your customer.experience will be..That’s what you need for reinvention,.and in my opinion,.companies who aren’t.already reinventing themselves.in some meaningful way are unwinding,.whether they realize it or not..The good news though is.that invention.and reinvention is very doable,.if you’re intentional.and focused on it..And we’ll be here every step.of the way to help you do it..I want to thank you for listening..I hope everybody stays safe,.and I hope you have.a great few weeks of re:Invent..Thank you very much..[music playing].[applause - cheering].

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