 From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Hello everyone, welcome to this special CUBE interview. We are here at theCUBE virtual covering AWS Summit virtual online. This is Amazon's summits that they normally do all around the world. They're doing them now virtually. We are here in the Palo Alto COVID-19 quarantine crew getting all the interviews here with special guests, vice president of machine learning. We have Swami Cube alumni who's been involved in not only the machine learning but all of the major activity around AWS around how machine learnings evolve and all the services around machine learning workflows from transcribe, recognition, you name it. Swami, you've been at the helm for many years and we've also chatted about that before. Welcome to the virtual CUBE covering AWS Summit. It's a pleasure to be here, Tom. Great to see you. I know times are tough, everything okay at Amazon. You guys are certainly cloud-scale, not too unfamiliar of working remotely. You have to do a lot of travel, but what's it like now for you guys right now? They're all actually doing well. We have been, I mean, many of you are working hard to make sure we continue to sell our customers even from AWS side. We have taken measures to prepare and we are confident that we'll be able to meet customer demands for capacity during this time. We are also helping customers do it out quickly and namely, current challenges. Various examples of amazing startups working in this area to reorganize themselves as a customer. We can talk about that one more later. Well, at large scale, you guys have done a great job and it's been fun watching and chronicling the journey of AWS as it now goes to a whole other level with the post-pandemic. We're expecting even more surge in everything from VPNs, workspaces, you name it. All these workloads are going to be under a lot of pressure to do more and more value. You've been at the heart of one of the key areas which is the tooling and the scale around machine learning workflows. And this is where customers are really trying to figure out what are the adequate tools? How do my teams effectively deploy machine learning? Because now more than ever, the data's going to start flowing in as virtualization, if you will, of life is happening. We're going to be in a hybrid world with life. We're going to be online most of the time. I think COVID-19 has proven that this new trajectory of virtualization, virtual work, applications are going to have to flex and adjust and scale and be reinvented. This is a key thing. What's going on with machine learning? What's new? Tell us what are you guys doing right now? Yeah, as you know, in AWS, we offer a broader system than we can get all the way from. Like expert practitioners, we offer our frameworks and infrastructure layer support for all popular frameworks from like TensorFlow, Apache M&M, and PyTor, shown CPUs are our own customers, like InferentShare. And then for aspiring ML developers who want to build their own custom machine learning models, we are actually building the offer SageMaker, which is our end-to-end machine learning service that makes it easy for customers to be able to build, train, tune, and apply machine learning models. And it is one of our fastest growing machine learning services and many startups and enterprises are starting to standardize their machine learning building on it. And then final tier is geared towards actually application developers who do not want to go into model building, just want an easy API to build capabilities that's a front-side, front-side recognition, and so forth. And I wanted to talk about one of the new capabilities we are about to launch pretty soon called Ketner. And so that's the key. So just from a new standpoint, that's GA now, that's being announced at the summit. That was a big hit at re-invent, Kendra. A lot of buzz. It's available. Yeah, so I'm excited to say that Kendra is our new machine learning-powered, highly accurate enterprise service that is made generally available. And if you look at what Kendra is, we have actually reimagined the traditional enterprise service, which is historically in an underserved market segment, so to speak. If you look at it on the public search, on the web search front, it is a relatively developed area where the enterprise search has been an area where within an enterprise, there are a huge amount of data silos spread in file systems, SharePoint or Salesforce or various other areas. And deploying a traditional search index as honest, the even simple questions like, when does an ID desk open? Or when, what is the paternity policy or so forth? These kind of things have been historically super hard to find within an enterprise. Let alone if I'm actually in a material science company or so forth, like what Trium was trying to do, table collaboration of researchers spread across the world. To search their experiment archives and so forth, it has been super hard for them to be able to do things. And this is one of those areas where Kendra has enabled the new, of course, where Kendra is a declining powered third service for enterprises, which breaks down data silos and collects actually data across various things, all the way from like on S3 or file system or SharePoint and various other data sources. And use the state of the art and how the techniques to be able to actually index them. And then you can query using natural language queries, such as like, when does my ID desk open? And the answer, it won't just give you a bunch of random like, tell you opens at 8.30 a.m. in the morning. Or what is the credit card, the cashback returns for my corporate credit card. It won't give you like a long list of links to it instead of give you answer to be two percent. So it's that much highly accurate. You know, people who have been in the enterprise search or data business know how hard this is. And, you know, it is super, it's been a super hard problem in the old, in the old garden models because databases were limiting to, you know, schemas and whatnot. Now you have a data driven world. And this has become interesting. I think the big takeaway I took away from Kendra was not only the new kind of discovery navigation that's possible in terms of low latency, getting relevant content, but it's really the under the covers impact. And I think I'd like to get your perspective on this because this has been an active conversation inside the community and cloud scale, which is data silos have been a problem. People have had built these data silos and they really talk about breaking them down, but it's real, again, hard. There's legacy problems and all applications that are tied to them. How do I break my silos down? Or how do I leverage either silos? So I think you guys really solve a problem here around data silos and scale. So talk about the data silos and then I'm going to follow up and get your take on the kind of size of data. Megabytes, petabytes, I mean, talk about data silos and the scale behind it. So if you look at actually how to set up something like the Kendra search cluster, even as a plus from your management console or the native because you'll be able to point Kendra to various data sources such as Amazon S3 or SharePoint and Salesforce and various others. And say, these are the kind of data I want to index. And Kendra automatically pulls in this data indexes using its deep learning and healthy models and then automatically builds a corpus. Then I, as a user of the search index can actually start querying it using natural language and don't have to worry where it comes from. And Kendra takes care of things like access control and it uses finally machine learning algorithms under the hood to understand the context of natural language query and return the most relevant. I'll give a real good example of some of the few customers who are using Kendra. For instance, if you take a look at 3M, 3M is using Kendra to support its material science R&DF by enabling natural language search of the expansive repositories of past research documents that may be relevant to new project. Imagine what this does to a company like 3M instead of researchers who are spread around the world repeating the same experiments on material research over and over again. Now their engineers and researchers will have the ability to quickly search through the document and they can innovate faster instead of trying to literally reinvent the wheel all the time. So it is better acceleration to the market. Even we are in this situation, one of the interesting work that you might be interested in is the semantics scholar team at Allen Institute for AI recently opened up what is a repository of scientific research called COVID-19 Open Research Data Set. These are expert research articles and that helps get domain. And now they index reducing Kendra and it helps scientists, academics and technologists to quickly find information in a sea of scientific literature. So you can even ask questions like how different is convalescent plasma treatment compared to vaccine and various index questions and Kendra automatically understand the context and gives the summary answer to these questions for the customers. So yeah, and this is one of the things where when we talk about breaking the data silos, it takes care of getting back the data and putting it in a central location, understanding the context behind each of these documents and then being able to also then quickly answer the priorities of customers using simple, plain natural language as well. So what's the scale? Talk about the scale behind this. What's the scale numbers? What are you guys seeing? Also you guys always do a good job having a great announcement and then following up with general availability, which means I know you've got some customers using it. What are we talking about in terms of scales? Pedabytes, can you give some insight into the kind of data scale you're talking about here? So the nice thing about Kendra is it is easily linearly scalable. So I as a developer, I can keep adding more and more data and it linearly scales to whatever scale our customers want. And that is one of the underpinnings of Kendra search engines. So this is where even if you see like customers like price order or scoopers is using Kendra to power it, regulate your application to help customer search through regulatory typically and easily. So instead of shifting through hundreds of pages of documents manually to answer certain questions, now Kendra allows them to answer natural language questions. I'll give another example, which is speech to the scale one is Baker Tilley leading advisory types and assurance forms using Kendra to index documents. Compared to a traditional SharePoint based food tech search, now they are using Kendra to quickly search product manuals and so forth. And they are able to get answers up to 10X faster. Look at that kind of impact what Kendra has, being able to index a vast amount of data that in a linearly scalable fashion keep adding it the order of terabytes and keep going and being able to search 10X faster than traditional, I mean, traditional keyword search based algorithm is actually a big deal for these customers. They're very excited. So what is the main problem that you're solving Kendra? What's the use case? If I'm the customer, what's my problem that you're solving? Is it just response to data, whether it's a call center or support, or is it an app? I mean, what's the main focus that you guys came out? What was the vector of problem that you're solving here? So maybe talk to customers before we started building Kendra. One of the things that constantly came back for us was that they wanted the same ease of use and the ability to search the worldwide web. Customers like those tools within an enterprise. It can be in the form of like an internal search to search within like the HR documents or internal Viki pages and so forth. Or it can be to search like internal technical documentation or the public documentation to help the contact centers or is it external search in terms of customer support and so forth or to enable collaboration by sharing knowledge base and so forth. So each of these really dissected, why is this a problem? Why is it not being solved by traditional search techniques? One of the things that became obvious was that unlike the external world, where the web pages are linked easily with very well-defined structure, internal world is very messy within an enterprise. The documents are put in a SharePoint or in a file system or in a storage service like S3 or on actually Salesforce or Vox or various other things. And what really customers wanted was a system which knows how to actually pull the data from various these data silos. Still understand the access control behind this and enforce them in the search and then understand the real data behind it and not just do simple keyword search so that you can build remarkable search service that really answers queries in a natural language. And this has been the key premise of Kendra and this is what is starting to resonate with our customers. I talked with some of the other examples even in areas like contact centers. For instance, Mygolin Health is using Kendra for its contact centers. So they are able to seamlessly tie, like member, provider, or client specific information with abundance and information about healthcare. So it's agent so that they can quickly resolve the call or it can be on internally to do things like external search as well. So very exciting. So you guys took the basic concept of discovery and navigation, which is the consumer web, find what you're looking for as fast as possible but also took advantage of building intelligence around understanding all the nuances and configuration, schemas, access under the covers and allowing things to be discovered in a new way. So you basically make data be discoverable and then provide an interface for discovery and navigation. So it's a broad use kind then. Yeah, that sounds about right. Except we did one thing more. We actually understood not just we didn't just do discovery and also made it easy for people to find the information but maybe we are switching through like terabytes or hundreds of terabytes of internal documentation. Sometimes one of the things that happens is throwing a bunch of hundreds of links to these documents is not good enough. For instance, if I'm actually trying to find out, for instance, what is the ALS marker in a healthcare setting? And for a particular research project then I don't want to actually sit through like thousands of links. Instead, I want to be able to correctly pinpoint which document contains answer to it. So that is the final element which is to really understand the context behind each and every document using our natural language processing techniques so that you're not only, I'm discovering the information that is relevant but you also get like highly accurate possible precise answers to some of your questions. Well, that's great stuff. Big fan. I was really liking the announcement of Kendra. Congratulations on the GA of that. We'll make some room on our cube virtual site for your team to put more Kendra information up. I think it's fascinating. I think that's going to be the beginning of how the world changes with this. Certainly with voice activation and API based applications integrating this in. I just see a ton of activity that this is going to have a lot of headroom. So appreciate that. The thing I want to get to while I have you here is the news around the augmented artificial intelligence has been brought out as well. So the GA of that is out. You guys are GAing everything which is right on track with your cadence of AWS law as I say. What is this about? Give us the headline story. What's the main thing to pay attention to of the GA? What have you learned? What's the learning curve? What's the results? So augmented artificial intelligence service. I call it H2I, but Amazon H2I service we made it generally available. And it is a very unique service that makes it easy for developers to augment human intelligence with machine learning predictions. And this is historically has been a very challenging problem. They look at, so let me take a step back and explain the general idea behind it. You look at any developer building a machine learning application. There are use cases where even actually a 99% accuracy in machine learning is not going to be good enough. You directly use that result as the response to back to the customer. Instead, you want to be able to augment that with human intelligence to make sure if my machine learning model is returning saying, hey, my confidence interval for this prediction is less than 70%. I would like it to be augmented with human intelligence. Then H2I makes it super easy for customers to be developers to use actually a human reviewer workflow that comes in between. So then I can actually send it either to the public who using mechanical talk where we have more than 500,000 talkers or I can use a private workforce or vendor workforce. So now H2I seamlessly integrates text-frag recognition or SageMaker custom models. So now, for instance, NHS is integrated H2I with text-frags so that when they are building these document processing workflows, the areas where the machine learning model confidence score is not as high, they will be able to augment that with their human reviewer workforce so that they can actually build an highly accurate document processing workflow as well. So this we think is an awful capability. So this really kind of gets to what I've been feeling in some of the stuff we've worked with you guys on our machine learning piece. It's hard for companies to hire machine learning people. This has been a real challenge. So I like this idea of human augmentation because humans and machines have to have that relationship. And if you build good abstraction layers and you abstract away the complexity, which is what you guys do. And that's the vision of cloud. Then you're going to need to have that relationship solidified. So at what point do you think we're going to be ready for the CUBE team or any customer that doesn't have to, can't find a machine learning person or may not want to pay the wages as required. I mean, it's hard to find a machine learning engineer. And when does the data science piece come in with visualization, the spectrum of, you know, pure computer science, math, machine learning guru to full end user productivity of machine learning is where you guys are doing a lot of work. Can you just share your opinion on that evolution and where we are on that? Because people want to get to the point where they don't have to hire machine learning folks. Yeah. And have that kind of sort of. So look at the history of technology. I actually always believe that many of these highly disruptive technology started as a way that it is available only to experts. And then they quickly go through cycles where it becomes almost commonplace. I'll give an example with something totally outside IT space. Let's take photography. I think more than probably 150 years ago, the first professional camera was invented and I took like three to four years to actually take a really good picture. And there were only very few expert photographers in the world. And then faster up to time where we are now. Now, even my five year old daughter takes actually very good portraits. I'd actually guess it as a gift to her mom for Mother's Day. So now, if you look at Instagram, everyone is a professional photographer. I kind of think it's about to, it will happen in machine learning too. Compared to 2012 where there were very few deep learning experts who can rebuild these amazing applications. Now you're starting to see like tens of thousands of machine customers using machine learning and production in 80 years. Not just proof of concepts, but in production. And this number is rapidly growing. I'll give one example. Literally if you see Amazon to aid our entire company to transform and make machine learning its natural part of the business. Six years ago, we started a machine learning university. And since then we have been training all our engineers to take machine learning courses in this ML University. And a year ago, we actually made these coursework available through our training and certification platform in AWS. And within 48 hours, more than 100,000 people registered. Think about it, that's like a big all time record. That's why I always like to believe that developers are always eager to learn. They're very hungry to pick up new technology. And I would be surprised if four or five years from now machine learning is kind of becomes a normal feature of the same way databases are. And it becomes less special. That day happens, then I would see it as my job is done. Well, you got a lot more work to do because I know from the conversations I've been having around this COVID-19 pandemic is that there's general consensus and validation that the future got pulled forward. And what used to be an inside industry conversation that we used to have around machine learning and some of the visions that you're talking about has been accelerated on the pace of the new cloud scale. But now that people now recognize that virtual and experiencing it firsthand globally, everyone, they're now going to be an acceleration of applications. So we believe there's going to be a Cambrian explosion of new applications that got to reimagine and reinvent some of the plumbing or abstractions in cloud to deliver new experiences because the expectations have changed. And I think one of the things we're seeing is that machine learning combined with cloud scale will create a whole new trajectory of a Cambrian explosion of applications. So this has kind of been validated. What's your reaction to that? I mean, do you see something similar? What are some of the things that you're seeing as we come into this world, this virtualization of our lives? It's every vertical. It's not one vertical anymore. That's maybe moving faster. I think everyone sees the impact. They see where the gaps are and there's new reality here. What's your thoughts? Yeah, if you see the history of machine learning specifically around deep learning, why the technology is really not new, I'd say, because the early deep learning paper was probably written like close to 30 years ago. And why didn't we see deep learning take off sooner? It is because historically deep learning technologies have been hungry for computer resources and hungry for like huge amount of data. And then the abstractions were not easy enough. I see rightfully pointed out that cloud has come in and made it super easy to get like access to huge amount of computer and huge amount of data. And you can literally pay by the hour or by the minute. And with new tools being made available to developers like SageMaker and all the AI services we are talking about, now there is an explosion of options available that are easy to use for developers that we are starting to see. Almost like a huge amount of like innovation starting to pop up. And unlike traditional disruptive technologies which we usually see practicing in like one or two industry segments and then it crosses the costum and then goes mainstream that machine learning, we are starting to see traction almost in like every industry segment all the way from like in financial sector, very fintech companies like Interior is using it to forecast it's call center volume and then personalization. In the healthcare sector, companies like AI Doc is using computer vision to assess radiologists. And then we are seeing in areas like public sector, NASA has partnered with, hated me as to use machine learning to do anomaly detection algorithms to detect solar flares in the space. And their examples are plenty. It is because now machine learning has become such commonplace that at almost every industry segment and every CIO is actually already looking at how can they reimagine and reinvent and make their customer experience better powered by machine learning. In the same way, Amazon actually asked itself like eight or 10 years ago. So very excited. Well, you know, you guys continue to do the work and I agree, it's not just machine learning by itself it's the integration and the perfect storm of elements that have come together at this time although pretty disastrous but I think ultimately it's going to come out we're going to come out of this and on a whole nother trajectory it's going to be a creativity will be emerged you can start seeing really those builders thinking okay, hey, I got to get out there I can deliver, solve the gaps, we are exposed solve the problems, pre-create new expectations new experience, I think it's going to be great for software developers I think it's going to change the computer science field and it's really bringing in the lifestyle aspect of things applications have to have a recognition of this convergence, this virtualization of life the application they're going to have to have that so remember virtualization helped Amazon form the cloud maybe we'll get some new kinds of virtualization Swami, thanks for coming on really appreciate it, always great to see you thanks for taking the time. Great to see you gentlemen, so thank you thanks again. Here's Swami, the vice president of machine learning at AWS been on before the CUBE alumni really sharing his insights around what we see around this virtualization, this online event I'll see Amazon summit, we're covering with the virtual CUBE but as we go forward, more important than ever the data is going to be important searching it, finding it and more importantly, having the humans use it building an application so the CUBE coverage continues ready for summit virtual online I'm John Furrier, thanks for watching.