 From around the globe, it's theCUBE with digital coverage of smart data marketplaces brought to you by IOTAHO. Hi everybody, this is Dave Vellante and welcome back. We've been talking about smart data. We've been hearing IOTAHO talk about putting data to work and a key part of building great data outcomes is the cloud, of course, and also cloud native tooling. Stuti Vishpande is here. She's a partner solutions architect for Amazon web services and an expert in this area. Stuti, great to see you. Thanks so much for coming on theCUBE. Thank you so much for having me here. You're very welcome. So let's talk a little bit about Amazon. I mean, you have been on this machine learning journey for quite some time. Take us through how this whole evolution has occurred in technology over the period of time since the cloud really has been evolving. Amazon in itself is a company, an example of a company that has gotten through a multi-year machine learning transformation to become the machine learning driven company that you see today. They have been improvising on original personalization model using robotics throughout the fulfillment centers, developing a forecasting system to predict the customer needs and improvising on that and meeting customer expectations on convenience, cost, delivery and speed. From developing natural language processing technology for end user interaction to developing a groundbreaking technology such as frying air drones to get packages to the customers. So our goal at Amazon web services is to take this rich expertise and experience with machine learning technology across Amazon and to work with thousands of customers and partners to hand over this powerful technology into the hands of developers or data engineers of all levels. Great, so okay, so if I'm a customer or partner of AWS, give me the sales pitch on why I should choose you for machine learning, what are the benefits that I'm going to get specifically from AWS? Well, there are three main reasons why partners choose us. First and foremost, we provide the broadest and the deepest set of machine learning and AI services and features for your business. The velocity at which we innovate is truly unmatched. Over the last year, we launched 200 different services and features. So not only our pace is accelerating, but we provide fully managed services to our customers and partners who can easily build sophisticated AI-driven applications and utilizing this fully managed services that they can build and train and deploy machine learning models, which is both valuable and differentiating. Secondly, we can accelerate the option of machine learning. So as I mentioned about fully managed services, for machine learning, we have Amazon SageMaker. So SageMaker is a fully managed service that any developer of any level or a data scientist can utilize to build complex machine learning algorithms and models and deploy that at scale with very less effort and at very less cost. Before SageMaker, it used to take so much of time and expertise and specialization to build all these extensive models. With SageMaker, you can literally build any complex models within just a time of days or weeks. So to increase adoption, AWS has exhalation programs, such as ML Solution Labs, and we also have education and training programs, such as DeepRacer, which enforces on enforcement learning and Embark, which actually help organization to adopt machine learning very readily. And we also support three major frameworks, such as TensorFlow, PyTorch, and we have separate teams who are dedicated to just focus on all these frameworks and improve the support of these frameworks for a wide variety of workloads. And thirdly, we provide the most comprehensive platform that is optimized for machine learning. So when you think about machine learning, you need to have a data store where you can store your training sets, your test sets, which is highly reliable, highly scalable, and secure data store. Most of our customers want to store all of their data and any kind of data into a centralized repository that can be treated as the central source of truth. And in this case, probably an Amazon S3 data store to build an end-to-end machine learning workflow. So we believe that we provide this capability of having the most comprehensive platform to build the machine learning workflow from end-to-end. Great, thank you for that. So my next question is, this is a complicated situation for a lot of customers. Having the technology is one thing, but adoption is sort of everything. So I wonder if you could paint a picture for us and help us understand how you're helping customers think about machine learning, thinking about that journey, and maybe give us the context of what the ecosystem looks like. Sure, if someone can put up the build, I would like to provide a picture representation of how AWS Envision Machine Learning has three layers of stack. And moving on to next build, I can talk about the bottom layer. And bottom layer, if you can see over the screen, it's basically for advanced technologists, advanced data scientists who are machine learning practitioners who work at the framework level. 90% of data scientists use multiple frameworks because multiple frameworks are adjusted and are suitable for multiple and different kinds of workloads. So at this layer, we provide support for all of the different kind of frameworks. And the bottom layer is only for the advanced scientists and developers who actually want to build, train, and deploy these machine learning models by themselves. And moving on to the next level, which is the middle layer, this layer is only suited for non-experts. So here we have SageMaker, where it provides a fully managed service where you can build, tune, train, and deploy your machine learning models at a very low cost and with very minimal efforts and at a higher scale. It removes all the complexity, heavy lifting and guesswork from this stage of machine learning. And Amazon SageMaker has been the C level change. Many of our customers are actually standardizing on top of Amazon SageMaker. And then moving on to the next layer, which is the top most layer, we call this as AI services because this may make the human cognition. So all the services mentioned here, such as Amazon Recognition, which is basically a deep learning service optimized for image and video analysis. And then we have Amazon Poly, which can do the text to speech conversion and so on and so forth. So these are the AI services that can be embedded into the application so that the end user or the end customer can build AI driven applications. Love it. Okay, so you got the experts at the bottom with the frameworks, the hardcore data scientists. You kind of got the self-driving machine learning in the middle. And then you have all the ingredients. I'm like an AI chef or a machine learning chef. I can pull in vision, speech, chatbots, fraud detection and sort of compile my own solutions. That's cool. We hear a lot about SageMaker, Stuti. I wonder if you could tell us a little bit more. Can we double click a little bit on SageMaker? That seems to be a pretty important component of that stack that you just showed us. Sure, and I think that was an absolutely very great summarization of all the different layers of machine learning stack. So thank you for providing the gist of that. Of course, I'll be really happy to talk about Amazon SageMaker because most of our customers are actually sanitizing on top of SageMaker. Jelki has spoken about how machine learning traditionally has so many complications and it's very complex and extensive, expensive and iterative process which makes it even harder because there are no integrated tools if you do the traditional machine learning kind of deployment. There are no integrated tools for the entire workflow process and deployment. And that is where SageMaker comes into the picture. SageMaker removes all the heavy lifting and complexities from each step of the deployment of machine learning workflow. How it solves these type challenges by providing all of the different components that are optimized for every stage of the workflow into one single toolset so that models get to production faster and with much less effort and at a lower cost. We really continue to add important capabilities to Amazon SageMaker. I think last year we announced over 50 capabilities in just for SageMaker to improvise its features and functionalities. And I would like to call out a couple of those here. SageMaker Notebooks which are just one click deployment Notebooks that comes along with easy to instances. I'm sorry for quoting Jarvan here. It's Amazon Elastic Compute Instances. So you just need to have a one click deployment and you have the entire SageMaker Notebook interface along with the Elastic Compute Instances running that gives you the faster time to production. If you're a machine, if you are a data scientist or a data engineer who works extensively for machine learning, you must be aware about building training datasets is really complex. So there we have Amazon Ground Truth that is only for building machine learning training datasets, which can reduce your labeling costs by 70%. And if you perform machine learning and aware about the technology in general, there are some workflows where you need to join inferences. So there we have Elastic Inference Instances where you can reduce the cost by 75% by adding a little GPU acceleration or you can reduce the cost by adding managed spot training utilizing easy to spot instances. So there are multiple ways where you can reduce the cost. And there are multiple ways where you can improvise and speed up your machine learning deployment and workflow. So one of the things I love about, I mean, I'm a prime member who's not, right? I love to shop at Amazon. And what I like about it is the consumer experience. It kind of helps me find things that maybe I wasn't aware of, maybe based on other patterns that are going on in the buying community or people that are similar. If I want to find a good book, it's always gives me great reviews and recommendations. So I'm wondering if that applies to sort of the tech world and machine learning, learning world. Are you seeing any patterns emerge across the various use cases? You have such scale, what can you tell us about that? Sure, one of the patterns that we have seen all the time is to build scalable layer for any kind of use case. So as I spoke before, that customers are really looking to put their data into a single set of repository where they have the single source of truth. So storing data and any kind of data at any velocity into a single source would actually help them to build models who run on these data and get useful insights out of it. So when we speak about an end-to-end workflow, using Amazon SageMaker along with a scalable analytical tool is actually what we have seen as one of the patterns where they can perform some analysis using Amazon SageMaker and build predictive models to say suppose if you want to take a healthcare use case. So they can build a predictive model that can determine the readmissions of using Amazon SageMaker. So what I mean to say is by not moving data around and connecting different services to the single set of source of data, customers avoid creating other copies of data, which is very crucial when you are having training data set and test data sets with Amazon SageMaker. It is highly important to consider this. So the pattern that we have seen is to utilize central source of repository of data, which could be Amazon S3 in this scenario, a scalable analytical layer along with SageMaker. I would like to quote an intuitive success story over here. Using Amazon SageMaker in Qt had reduced machine learning deployment time by 90%. So I'm quoting here from six months to one week. And if you think about healthcare industry, there have been a shift from reactive to predictive care. So utilizing predictive models to accelerate research and discovery of new jobs and new treatments. And we have also observed that nurses were supported by AI tools increased, their productivity has increased by 50%. I would like to say that one of our customers are really diving deep into the AWSD portfolio of machine learning and AI services. And including transcribed medical, where they are able to provide some insights so that their customers are getting benefits from them. Most of their customers are healthcare providers and they are able to give some insights so that they can create some more personalized and improvised patient care. So there you have the end user benefits as well. One of the patterns that I have, I can speak about and what we have seen as well, pairing a predictive model with real-time integration into healthcare records will actually help the healthcare provider customers for informed decision making and improvising the personalized patient care. You know, that's a great example, several there, and I appreciate that. I mean, healthcare is one of those industries that is just so right for technology ingestion and transformation. And it's a great example of how the cloud has really enabled really, I mean, talking about major changes in healthcare with proactive versus reactive. We're talking about lower costs, better health, longer lives, just really inspiring to see that evolve. We're going to watch it over the next several years. I wonder if we could close on the marketplace. I've had the pleasure of interviewing Dave McCann a number of times. He and his team have built just an awesome capability for Amazon and its ecosystem. What about the data products? Whether it's SageMaker or other data products in the marketplace, what can you tell us? Sure, AWS Marketplace is an interesting thing. So let me first talk about the AWS Marketplace. With AWS Marketplace, you can browse and search for hundreds of machine learning algorithms and machine learning model packages in a broad range of categories, such as computer vision, fixed analysis, voice analysis, image and video analysis, predictive models and so on and so forth. And all of these models and algorithms can be deployed to a Jupyter Notebook which comes as part of the SageMaker platform. And you can integrate all of these different models and algorithms into our fully managed service which is Amazon SageMaker through Jupyter Notebooks, SageMaker, SDK and even command line as well. And this experience is followed by AWS Marketplace catalog and APIs. So you get the same benefits as any other marketplace products which is seamless deployments and consolidated billing. So you get the same benefits as the products in the AWS Marketplace for your machine learning algorithms and model packages. And this is really important because these can be directly integrated into our SageMaker platform. And I don't even ask about the data products as well and I'll be really happy to provide and quote one of the example over here. In the interest of COVID times and because we are in the unprecedented times over here, we collaborated with our partners to provide some data products and one of them is Data Hub by Capitol Hill that gives you the time series data of cases and debt that are gathered from multiple trusted sources. And this is to provide better and informed knowledge so that everyone who was utilizing this product can make some infocussions and help the community at the end. I love it, I love this concept of being able to access the data, algorithms, tooling and it's not just about the data, it's being able to do something with the data and that we've been talking about injecting intelligence into those data marketplaces. That's what we mean by smart data marketplaces. Stuti Deshpande, thanks so much for coming to theCUBE, sharing your knowledge and tell us a little bit about AWS. It was a pleasure having you. It was my pleasure too. Thank you so much for having me here. Are you interested in test driving the IOTAHO platform? Kickstart the benefits of data automation for your business through the IOLabs program. A flexible, scalable sandbox environment on the cloud of your choice, with setup, service and support provided by IOTAHO. Click on the link and connect with a data engineer to learn more and see IOTAHO in action.