>> Announcer: From around the globe it's theCUBE with digital coverage of smart data marketplaces brought to you by Io Tahoe. >> Hi everybody, this is Dave Vellante. And welcome back. We've been talking about smart data. We've been hearing Io Tahoe talk about putting data to work and keep heart of building great data outcomes is the Cloud of course, and also Cloud native tooling. Stuti Deshpande 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 sometime. 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 to all different women's centers, developing a forecasting system to predict the customer needs and improvising on that and reading customer expectations on convenience, fast delivery and speed, from developing natural language processing technology for end user infraction, to developing a groundbreaking technology such as Prime Air jobs to give packages to the customers. So our goal at Amazon With 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 handle this powerful technology into the hands of developers or data engineers of all levels. >> Great. So, okay. So if I'm a customer or a 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 those fully managed services began build and train and deploy machine learning models, which is both valuable and differentiating. Secondly, we can accelerate the adoption 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 are 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 a very less cost. Before SageMaker, it used to take so much of time and expertise and specialization to build all these extensive models, but SageMaker, you can literally build any complex models within just a time of days or weeks. So to increase it option, AWS has acceleration programs just in a solution maps. And we also have education and training programs such as DeepRacer, which are 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 five charge, or they 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 finaly, 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 at the central source of fraud. And in this case from the Amazon Esri data store to build and endurance machine learning workflow. So we believe that we provide this capability of having the most comprehensive platform to build the machine learning workflow from internally. >> Great. Thank you for that. So I wanted, my next question is, this is a complicated situation for a lot of customers. You know, 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 belt, I would like to provide a picture representation of how AWS and fusion machine learning as three layers of stack. And moving on to next bill, I can talk about the bottom there. And bottom there as you can see over this 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 types of frameworks. And the bottom layer is only for the advanced scientists and developers who are actually actually want to build, train and deploy these machine learning models by themselves and moving onto 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 there 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 scene that will change. Many of our customers are actually standardizing on top off Amazon SageMaker. And then I'm moving on to the next layer, which is the top most layer. We call this as AI services because this may make the human recognition. So all of the services mentioned here such as Amazon Rekognition, which is basically a deep learning service optimized for image and video analysis. And then we have Amazon Polly, 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've got the experts at the bottom with the frameworks, the hardcore data scientists, you kind of get 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 studio. 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. >> I think that was an absolutely very great summarization of all the different layers of machine unexpected. 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 standardizing on top of SageMaker. That is spoken about how machine learning traditionally has so many complications and it's very complex and expensive and I traded process, which makes it even harder because they don't know integrated tools or if you do the traditional machine learning all 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 heaviness thing and complexities from each step of the deployment of machine learning workflow, how it solves our challenges by providing all of the different components that are optimized for every stage of the workflow into one single tool set. So that models get to production faster and with much less effort and at a lower cost. We really continue to add important (indistinct) leading to Amazon SageMaker. I think last year we announced 50 cubic litres in this far SageMaker being improvised it's features and functionalities. And I would love to call out a couple of those here, SageMaker notebooks, which are just one thing, the prominent notebooks that comes along with easy two instances, I'm sorry for quoting Jarvin here is Amazon Elastic Compute Instances. So you just need to have a one thing 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 worked extensively for machine learning, you must be aware about building training datasets is really complex. So there we have on his own ground truth, that is only for building machine learning training data sets, which can reduce your labeling cost by 70%. And if you perform machine learning and other model technology in general, there are some workflows where you need to do inferences. So there we have inference, Elastic Inference Incense, which you can reduce the cost by 75% by adding a little GP acceleration. Or you can reduce the cost by adding managed squad training, utilizing easy to spot instances. So there are multiple ways that you can reduce the costs and there are multiple ways there 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 is 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 with 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, 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 battles that we have seen all the time is to build scalable layer for any kind of use case. So as I spoke before that as much, I'm really looking to put their data into a single set of depository where they have the single source of truth. So storing of data and any kind of data at any velocity into a single source of would actually help them build models who run on these data and get useful insights out of it. So when you speak about an entry and workflow, using Amazon SageMaker along bigger, scalable analytical tool is actually what we have seen as one of the factors where they can perform some analysis using Amazon SageMaker and build predictive models to say samples, if you want to take a healthcare use case. So they can build a predictive model that can victimize the readmissions of using Amazon SageMaker. So what I mean, to say is, by not moving data around and connecting different services to the same set of source of data, that's tumor avoid creating copies of data, which is very crucial when you are having training data set and test data sets with Amazon SageMaker. And it is highly important to consider this. So the pattern that we have seen is to utilize a central source of depository of data, which could be Amazon Extra. In this scenario, scalable analytical layer along with SageMaker. I would have to code at Intuit for a success story over here. I'm using sandwich, a Amazon SageMaker Intuit had reviews the machine learning deployment time by 90%. So I'm quoting here from six months to one week. And if you think about a healthcare industry, there hadn't been a shift from reactive to predictive care. So utilizing predictive models to accelerate research and discovery of new drugs and new treatments. And you've also observed that nurses were supported by AI tools increase their, their productivity has increased by 50%. I would like to say that one of our customers are really diving deep into the AWS 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 into insights so that they can create some more personalized and improvise 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, appearing a predictive model with real time integration into healthcare records will actually help their healthcare provider customers for informed decision making and improvising the personalized patient care. >> 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, that is a great example of how the cloud has really enabled really. I mean, I'm talking about major changes in healthcare with proactive versus reactive. We're talking about lower costs, better health, longer lives is really inspiring to see that evolve. We're going to watch it over the next several years. I wonder if we could close in 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. Either of this market visits are interesting thing. So let me first talk about the AWS marketplace of what, AWS marketplace you can browse and search for hundreds of machine learning algorithms and machine learning, modern packages in a broad range of categories that this company provision, fixed analysis, voice answers, email, video, and it says predictive models and so on and so forth. And all of these models and algorithms can be deployed to a Jupiter notebook, which comes as part of the SageMaker that form. And you can integrate all of these different models and algorithms into our fully managed service, which is Amazon SageMaker to Jupiter notebooks, Sage maker, STK, and even command as well. And this experience is followed by either of those marketplace catalog and API. So you get the same benefits as any other marketplace products, the just seamless deployments and consolidate it. So you get the same benefits as the products and the invest marketplace for your machine learning algorithms and model packages. And this is really important because these can be darkly integrated into our SageMaker platform. And I don't even be honest about the data products as well. And I'm really happy to provide and code one of the example over here in the interest of cooler times and because we are in unprecedented times over here we collaborated with our partners to provide some data products. And one of them is data hub by tablet view that gives you the time series data of phases and depth data 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 informed decisions 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 theCUBES here, sharing your knowledge and tell us a little bit about AWS. There's a pleasure having you. >> It's my pleasure too. Thank you so much for having me here. >> You're very welcome. And thank you for watching. Keep it right there. We will be right back right after this short break. (soft orchestral music)

>> Announcer: From around the globe it's theCUBE with digital coverage of smart data marketplaces brought to you by Io Tahoe. >> Hi everybody, this is Dave Vellante. And welcome back. We've been talking about smart data. We've been hearing Io Tahoe talk about putting data to work and keep heart of building great data outcomes is the Cloud of course, and also Cloud native tooling. Stuti Deshpande 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 sometime. 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 to all different women's centers, developing a forecasting system to predict the customer needs and improvising on that and reading customer expectations on convenience, fast delivery and speed, from developing natural language processing technology for end user infraction, to developing a groundbreaking technology such as Prime Air jobs to give packages to the customers. So our goal at Amazon With 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 handle this powerful technology into the hands of developers or data engineers of all levels. >> Great. So, okay. So if I'm a customer or a 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 those fully managed services began build and train and deploy machine learning models, which is both valuable and differentiating. Secondly, we can accelerate the adoption 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 are 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 a very less cost. Before SageMaker, it used to take so much of time and expertise and specialization to build all these extensive models, but SageMaker, you can literally build any complex models within just a time of days or weeks. So to increase it option, AWS has acceleration programs just in a solution maps. And we also have education and training programs such as DeepRacer, which are enforces on enforcement learning and Embark, which actually help organization to adopt machine learning very readily. And we also support three major frameworks that just tensive no charge, or they 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 finaly, 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 at the central source of fraud. And in this case from the Amazon Esri data store to build and endurance machine learning workflow. So we believe that we provide this capability of having the most comprehensive platform to build the machine learning workflow from internally. >> Great. Thank you for that. So I wanted, my next question is, this is a complicated situation for a lot of customers. You know, 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 belt, I would like to provide a picture representation of how AWS and fusion machine learning as three layers of stack. And moving on to next bill, I can talk about the bottom there. And bottom there as you can see over this 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 types of frameworks. And the bottom layer is only for the advanced scientists and developers who are actually actually want to build, train and deploy these machine learning models by themselves and moving onto the next level, which is the middle layer. This layer is only suited for non-experts. So here we have seen Jamaica where it provides a fully managed service there 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, having a thing and guess guesswork from this stage of machine learning and Amazon SageMaker has been the scene that will change. Many of our customers are actually standardizing on top off Amazon SageMaker. And then I'm moving on to the next layer, which is the top most layer. We call this as AI services because this may make the human recognition. So all of the services mentioned here such as Amazon Rekognition, which is basically a deep learning service optimized for image and video analysis. And then we have Amazon Polly, which can do the text to speech from Russian 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've got the experts at the bottom with the frameworks, the hardcore data scientists, you kind of get 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 studio. 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. >> I think that was an absolutely very great summarization of all the different layers of machine unexpected. 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 standardizing on top of SageMaker. That is spoken about how machine learning traditionally has so many complications and it's very complex and expensive and I traded process, which makes it even harder because they don't know integrated tools or if you do the traditional machine learning all 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 heaviness thing and complexities from each step of the deployment of machine learning workflow, how it solves our challenges by providing all of the different components that are optimized for every stage of the workflow into one single tool set. So that models get to production faster and with much less effort and at a lower cost. We really continue to add important (indistinct) leading to Amazon SageMaker. I think last year we announced 50 cubic litres in this far SageMaker being improvised it's features and functionalities. And I would love to call out a couple of those here, SageMaker notebooks, which are just one thing, the prominent notebooks that comes along with easy two instances, I'm sorry for quoting Jarvin here is Amazon Elastic Compute Instances. So you just need to have a one thing 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 worked extensively for machine learning, you must be aware about building training datasets is really complex. So there we have on his own ground truth, that is only for building machine learning training data sets, which can reduce your labeling cost by 70%. And if you perform machine learning and other model technology in general, there are some workflows where you need to do inferences. So there we have inference, Elastic Inference Incense, which you can reduce the cost by 75% by adding a little GP acceleration. Or you can reduce the cost by adding managed squad training, utilizing easy to spot instances. So there are multiple ways that you can reduce the costs and there are multiple ways there 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 is 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 with 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, 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 battles that we have seen all the time is to build scalable layer for any kind of use case. So as I spoke before that as much, I'm really looking to put their data into a single set of depository where they have the single source of truth. So storing of data and any kind of data at any velocity into a single source of would actually help them build models who run on these data and get useful insights out of it. So when you speak about an entry and workflow, using Amazon SageMaker along bigger, scalable analytical tool is actually what we have seen as one of the factors where they can perform some analysis using Amazon SageMaker and build predictive models to say samples, if you want to take a healthcare use case. So they can build a predictive model that can victimize the readmissions of using Amazon SageMaker. So what I mean, to say is, by not moving data around and connecting different services to the same set of source of data, that's tumor avoid creating copies of data, which is very crucial when you are having training data set and test data sets with Amazon SageMaker. And it is highly important to consider this. So the pattern that we have seen is to utilize a central source of depository of data, which could be Amazon Extra. In this scenario, scalable analytical layer along with SageMaker. I would have to code at Intuit for a success story over here. I'm using sandwich, a Amazon SageMaker Intuit had reviews the machine learning deployment time by 90%. So I'm quoting here from six months to one week. And if you think about a healthcare industry, there hadn't been a shift from reactive to predictive care. So utilizing predictive models to accelerate research and discovery of new drugs and new treatments. And you've also observed that nurses were supported by AI tools increase their, their productivity has increased by 50%. I would like to say that one of our customers are really diving deep into the AWS 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 into insights so that they can create some more personalized and improvise 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, appearing a predictive model with real time integration into healthcare records will actually help their healthcare provider customers for informed decision making and improvising the personalized patient care. >> 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, that is a great example of how the cloud has really enabled really. I mean, I'm talking about major changes in healthcare with proactive versus reactive. We're talking about lower costs, better health, longer lives is really inspiring to see that evolve. We're going to watch it over the next several years. I wonder if we could close in 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. Either of this market visits are interesting thing. So let me first talk about the AWS marketplace of what, AWS marketplace you can browse and search for hundreds of machine learning algorithms and machine learning, modern packages in a broad range of categories that this company provision, fixed analysis, voice answers, email, video, and it says predictive models and so on and so forth. And all of these models and algorithms can be deployed to a Jupiter notebook, which comes as part of the SageMaker that form. And you can integrate all of these different models and algorithms into our fully managed service, which is Amazon SageMaker to Jupiter notebooks, Sage maker, STK, and even command as well. And this experience is followed by either of those marketplace catalog and API. So you get the same benefits as any other marketplace products, the just seamless deployments and consolidate it. So you get the same benefits as the products and the invest marketplace for your machine learning algorithms and model packages. And this is really important because these can be darkly integrated into our SageMaker platform. And I don't even be honest about the data products as well. And I'm really happy to provide and code one of the example over here in the interest of cooler times and because we are in unprecedented times over here we collaborated with our partners to provide some data products. And one of them is data hub by tablet view that gives you the time series data of phases and depth data 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 informed decisions 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 theCUBES here, sharing your knowledge and tell us a little bit about AWS. There's a pleasure having you. >> It's my pleasure too. Thank you so much for having me here. >> You're very welcome. And thank you for watching. Keep it right there. We will be right back right after this short break. (soft orchestral music)

>> Narrator: From around the globe, it's the CUBE with digital coverage of Smart Data Marketplaces, brought to you by Io-Tahoe. >> Hello this is Dave Vellante of the CUBE inviting you to join me for a special drill down presentation on the importance of automated data migration. Along with our friends from Io-Tahoe, we're going to explore the recent trends of automated data discovery, adaptive data governance, and just how far we've come from manually curating an enterprise data catalog. Ajay Vahora is the CEO of Io-Tahoe, as well as Stuti Deshpande of AWS and the digital evangelist Ved Sen of TCS, Tata Consultancy Services, will be there as well. Hope you can join us on Thursday, September 17th, at 9:00 a.m. Pacific for Smart Data Marketplaces. For more details, click on theCUBE.net. (upbeat music)