>> Okay, we're now going to explore the vision of the future of cloud computing from the perspective of one of the leaders in the field, JG Chirapurath is the Vice President of Azure Data AI and Edge at Microsoft. JG, welcome to theCUBE on Cloud, thanks so much for participating. >> Well, thank you, Dave. And it's a real pleasure to be here with you and just want to welcome the audience as well. >> Well, JG, judging from your title, we have a lot of ground to cover and our audience is definitely interested in all the topics that are implied there. So let's get right into it. We've said many times in theCUBE that the new innovation cocktail comprises machine intelligence or AI applied to troves of data with the scale of the cloud. It's no longer we're driven by Moore's law. It's really those three factors and those ingredients are going to power the next wave of value creation in the economy. So first, do you buy into that premise? >> Yes, absolutely. We do buy into it and I think one of the reasons why we put data analytics and AI together, is because all of that really begins with the collection of data and managing it and governing it, unlocking analytics in it. And we tend to see things like AI, the value creation that comes from AI as being on that continuum of having started off with really things like analytics and proceeding to be machine learning and the use of data in interesting ways. >> Yes, I'd like to get some more thoughts around data and how you see the future of data and the role of cloud and maybe how Microsoft strategy fits in there. I mean, your portfolio, you've got SQL Server, Azure SQL, you got Arc which is kind of Azure everywhere for people that aren't familiar with that you got Synapse which course does all the integration, the data warehouse and it gets things ready for BI and consumption by the business and the whole data pipeline. And then all the other services, Azure Databricks, you got you got Cosmos in there, you got Blockchain, you've got Open Source services like PostgreSQL and MySQL. So lots of choices there. And I'm wondering, how do you think about the future of cloud data platforms? It looks like your strategy is right tool for the right job. Is that fair? >> It is fair, but it's also just to step back and look at it. It's fundamentally what we see in this market today, is that customers they seek really a comprehensive proposition. And when I say a comprehensive proposition it is sometimes not just about saying that, "Hey, listen "we know you're a sequence of a company, "we absolutely trust that you have the best "Azure SQL database in the cloud. "But tell us more." We've got data that is sitting in Hadoop systems. We've got data that is sitting in PostgreSQL, in things like MongoDB. So that open source proposition today in data and data management and database management has become front and center. So our real sort of push there is when it comes to migration management modernization of data to present the broadest possible choice to our customers, so we can meet them where they are. However, when it comes to analytics, one of the things they ask for is give us lot more convergence use. It really, it isn't about having 50 different services. It's really about having that one comprehensive service that is converged. That's where things like Synapse fits in where you can just land any kind of data in the lake and then use any compute engine on top of it to drive insights from it. So fundamentally, it is that flexibility that we really sort of focus on to meet our customers where they are. And really not pushing our dogma and our beliefs on it but to meet our customers according to the way they've deployed stuff like this. >> So that's great. I want to stick on this for a minute because when I have guests on like yourself they never want to talk about the competition but that's all we ever talk about. And that's all your customers ever talk about. Because the counter to that right tool for the right job and that I would say is really kind of Amazon's approach is that you got the single unified data platform, the mega database. So it does it all. And that's kind of Oracle's approach. It sounds like you want to have your cake and eat it too. So you got the right tool with the right job approach but you've got an integration layer that allows you to have that converged database. I wonder if you could add color to that and confirm or deny what I just said. >> No, that's a very fair observation but I'd say there's a nuance in what I sort of described. When it comes to data management, when it comes to apps, we have then customers with the broadest choice. Even in that perspective, we also offer convergence. So case in point, when you think about cosmos DB under that one sort of service, you get multiple engines but with the same properties. Right, global distribution, the five nines availability. It gives customers the ability to basically choose when they have to build that new cloud native app to adopt cosmos DB and adopt it in a way that is an choose an engine that is most flexible to them. However, when it comes to say, writing a SequenceServer for example, if modernizing it, you want sometimes, you just want to lift and shift it into things like IS. In other cases, you want to completely rewrite it. So you need to have the flexibility of choice there that is presented by a legacy of what sits on premises. When you move into things like analytics, we absolutely believe in convergence. So we don't believe that look, you need to have a relational data warehouse that is separate from a Hadoop system that is separate from say a BI system that is just, it's a bolt-on. For us, we love the proposition of really building things that are so integrated that once you land data, once you prep it inside the Lake you can use it for analytics, you can use it for BI, you can use it for machine learning. So I think, our sort of differentiated approach speaks for itself there. >> Well, that's interesting because essentially again you're not saying it's an either or, and you see a lot of that in the marketplace. You got some companies you say, "No, it's the data lake." And others say "No, no, put it in the data warehouse." And that causes confusion and complexity around the data pipeline and a lot of cutting. And I'd love to get your thoughts on this. A lot of customers struggle to get value out of data and specifically data product builders are frustrated that it takes them too long to go from, this idea of, hey, I have an idea for a data service and it can drive monetization, but to get there you got to go through this complex data life cycle and pipeline and beg people to add new data sources and do you feel like we have to rethink the way that we approach data architecture? >> Look, I think we do in the cloud. And I think what's happening today and I think the place where I see the most amount of rethink and the most amount of push from our customers to really rethink is the area of analytics and AI. It's almost as if what worked in the past will not work going forward. So when you think about analytics only in the enterprise today, you have relational systems, you have Hadoop systems, you've got data marts, you've got data warehouses you've got enterprise data warehouse. So those large honking databases that you use to close your books with. But when you start to modernize it, what people are saying is that we don't want to simply take all of that complexity that we've built over, say three, four decades and simply migrate it en masse exactly as they are into the cloud. What they really want is a completely different way of looking at things. And I think this is where services like Synapse completely provide a differentiated proposition to our customers. What we say there is land the data in any way you see, shape or form inside the lake. Once you landed inside the lake, you can essentially use a Synapse Studio to prep it in the way that you like. Use any compute engine of your choice and operate on this data in any way that you see fit. So case in point, if you want to hydrate a relational data warehouse, you can do so. If you want to do ad hoc analytics using something like Spark, you can do so. If you want to invoke Power BI on that data or BI on that data, you can do so. If you want to bring in a machine learning model on this prep data, you can do so. So inherently, so when customers buy into this proposition, what it solves for them and what it gives to them is complete simplicity. One way to land the data multiple ways to use it. And it's all integrated. >> So should we think of Synapse as an abstraction layer that abstracts away the complexity of the underlying technology? Is that a fair way to think about it? >> Yeah, you can think of it that way. It abstracts away Dave, a couple of things. It takes away that type of data. Sort of complexities related to the type of data. It takes away the complexity related to the size of data. It takes away the complexity related to creating pipelines around all these different types of data. And fundamentally puts it in a place where it can be now consumed by any sort of entity inside the Azure proposition. And by that token, even Databricks. You can in fact use Databricks in sort of an integrated way with the Azure Synapse >> Right, well, so that leads me to this notion of and I wonder if you buy into it. So my inference is that a data warehouse or a data lake could just be a node inside of a global data mesh. And then it's Synapse is sort of managing that technology on top. Do you buy into that? That global data mesh concept? >> We do and we actually do see our customers using Synapse and the value proposition that it brings together in that way. Now it's not where they start, oftentimes when a customer comes and says, "Look, I've got an enterprise data warehouse, "I want to migrate it." Or "I have a Hadoop system, I want to migrate it." But from there, the evolution is absolutely interesting to see. I'll give you an example. One of the customers that we're very proud of is FedEx. And what FedEx is doing is it's completely re-imagining its logistics system. That basically the system that delivers, what is it? The 3 million packages a day. And in doing so, in this COVID times, with the view of basically delivering on COVID vaccines. One of the ways they're doing it, is basically using Synapse. Synapse is essentially that analytic hub where they can get complete view into the logistic processes, way things are moving, understand things like delays and really put all of that together in a way that they can essentially get our packages and these vaccines delivered as quickly as possible. Another example, it's one of my favorite. We see once customers buy into it, they essentially can do other things with it. So an example of this is really my favorite story is Peace Parks initiative. It is the premier of white rhino conservancy in the world. They essentially are using data that has landed in Azure, images in particular to basically use drones over the vast area that they patrol and use machine learning on this data to really figure out where is an issue and where there isn't an issue. So that this part with about 200 radios can scramble surgically versus having to range across the vast area that they cover. So, what you see here is, the importance is really getting your data in order, landing consistently whatever the kind of data it is, build the right pipelines, and then the possibilities of transformation are just endless. >> Yeah, that's very nice how you worked in some of the customer examples and I appreciate that. I want to ask you though that some people might say that putting in that layer while you clearly add simplification and is I think a great thing that there begins over time to be a gap, if you will, between the ability of that layer to integrate all the primitives and all the piece parts, and that you lose some of that fine grain control and it slows you down. What would you say to that? >> Look, I think that's what we excel at and that's what we completely sort of buy into. And it's our job to basically provide that level of integration and that granularity in the way that it's an art. I absolutely admit it's an art. There are areas where people crave simplicity and not a lot of sort of knobs and dials and things like that. But there are areas where customers want flexibility. And so I think just to give you an example of both of them, in landing the data, in consistency in building pipelines, they want simplicity. They don't want complexity. They don't want 50 different places to do this. There's one way to do it. When it comes to computing and reducing this data, analyzing this data, they want flexibility. This is one of the reasons why we say, "Hey, listen you want to use Databricks. "If you're buying into that proposition. "And you're absolutely happy with them, "you can plug it into it." You want to use BI and essentially do a small data model, you can use BI. If you say that, "Look, I've landed into the lake, "I really only want to use ML." Bring in your ML models and party on. So that's where the flexibility comes in. So that's sort of that we sort of think about it. >> Well, I like the strategy because one of our guests, Jumark Dehghani is I think one of the foremost thinkers on this notion of of the data mesh And her premise is that the data builders, data product and service builders are frustrated because the big data system is generic to context. There's no context in there. But by having context in the big data architecture and system you can get products to market much, much, much faster. So, and that seems to be your philosophy but I'm going to jump ahead to my ecosystem question. You've mentioned Databricks a couple of times. There's another partner that you have, which is Snowflake. They're kind of trying to build out their own DataCloud, if you will and GlobalMesh, and the one hand they're a partner on the other hand they're a competitor. How do you sort of balance and square that circle? >> Look, when I see Snowflake, I actually see a partner. When we see essentially we are when you think about Azure now this is where I sort of step back and look at Azure as a whole. And in Azure as a whole, companies like Snowflake are vital in our ecosystem. I mean, there are places we compete, but effectively by helping them build the best Snowflake service on Azure, we essentially are able to differentiate and offer a differentiated value proposition compared to say a Google or an AWS. In fact, that's been our approach with Databricks as well. Where they are effectively on multiple clouds and our opportunity with Databricks is to essentially integrate them in a way where we offer the best experience the best integrations on Azure Berna. That's always been our focus. >> Yeah, it's hard to argue with the strategy or data with our data partner and ETR shows Microsoft is both pervasive and impressively having a lot of momentum spending velocity within the budget cycles. I want to come back to AI a little bit. It's obviously one of the fastest growing areas in our survey data. As I said, clearly Microsoft is a leader in this space. What's your vision of the future of machine intelligence and how Microsoft will participate in that opportunity? >> Yeah, so fundamentally, we've built on decades of research around essentially vision, speech and language. That's been the three core building blocks and for a really focused period of time, we focused on essentially ensuring human parity. So if you ever wonder what the keys to the kingdom are, it's the boat we built in ensuring that the research or posture that we've taken there. What we've then done is essentially a couple of things. We've focused on essentially looking at the spectrum that is AI. Both from saying that, "Hey, listen, "it's got to work for data analysts." We're looking to basically use machine learning techniques to developers who are essentially, coding and building machine learning models from scratch. So for that select proposition manifest to us as really AI focused on all skill levels. The other core thing we've done is that we've also said, "Look, it'll only work as long "as people trust their data "and they can trust their AI models." So there's a tremendous body of work and research we do and things like responsible AI. So if you asked me where we sort of push on is fundamentally to make sure that we never lose sight of the fact that the spectrum of AI can sort of come together for any skill level. And we keep that responsible AI proposition absolutely strong. Now against that canvas Dave, I'll also tell you that as Edge devices get way more capable, where they can input on the Edge, say a camera or a mic or something like that. You will see us pushing a lot more of that capability onto the edge as well. But to me, that's sort of a modality but the core really is all skill levels and that responsibility in AI. >> Yeah, so that brings me to this notion of, I want to bring an Edge and hybrid cloud, understand how you're thinking about hybrid cloud, multicloud obviously one of your competitors Amazon won't even say the word multicloud. You guys have a different approach there but what's the strategy with regard to hybrid? Do you see the cloud, you're bringing Azure to the edge maybe you could talk about that and talk about how you're different from the competition. >> Yeah, I think in the Edge from an Edge and I even I'll be the first one to say that the word Edge itself is conflated. Okay, a little bit it's but I will tell you just focusing on hybrid, this is one of the places where, I would say 2020 if I were to look back from a COVID perspective in particular, it has been the most informative. Because we absolutely saw customers digitizing, moving to the cloud. And we really saw hybrid in action. 2020 was the year that hybrid sort of really became real from a cloud computing perspective. And an example of this is we understood that it's not all or nothing. So sometimes customers want Azure consistency in their data centers. This is where things like Azure Stack comes in. Sometimes they basically come to us and say, "We want the flexibility of adopting "flexible button of platforms let's say containers, "orchestrating Kubernetes "so that we can essentially deploy it wherever you want." And so when we designed things like Arc, it was built for that flexibility in mind. So, here's the beauty of what something like Arc can do for you. If you have a Kubernetes endpoint anywhere, we can deploy an Azure service onto it. That is the promise. Which means, if for some reason the customer says that, "Hey, I've got "this Kubernetes endpoint in AWS. And I love Azure SQL. You will be able to run Azure SQL inside AWS. There's nothing that stops you from doing it. So inherently, remember our first principle is always to meet our customers where they are. So from that perspective, multicloud is here to stay. We are never going to be the people that says, "I'm sorry." We will never say (speaks indistinctly) multicloud but it is a reality for our customers. >> So I wonder if we could close, thank you for that. By looking back and then ahead and I want to put forth, maybe it's a criticism, but maybe not. Maybe it's an art of Microsoft. But first, you did Microsoft an incredible job at transitioning its business. Azure is omnipresent, as we said our data shows that. So two-part question first, Microsoft got there by investing in the cloud, really changing its mindset, I think and leveraging its huge software estate and customer base to put Azure at the center of it's strategy. And many have said, me included, that you got there by creating products that are good enough. We do a one Datto, it's still not that great, then a two Datto and maybe not the best, but acceptable for your customers. And that's allowed you to grow very rapidly expand your market. How do you respond to that? Is that a fair comment? Are you more than good enough? I wonder if you could share your thoughts. >> Dave, you hurt my feelings with that question. >> Don't hate me JG. (both laugh) We're getting it out there all right, so. >> First of all, thank you for asking me that. I am absolutely the biggest cheerleader you'll find at Microsoft. I absolutely believe that I represent the work of almost 9,000 engineers. And we wake up every day worrying about our customer and worrying about the customer condition and to absolutely make sure we deliver the best in the first attempt that we do. So when you take the plethora of products we deliver in Azure, be it Azure SQL, be it Azure Cosmos DB, Synapse, Azure Databricks, which we did in partnership with Databricks, Azure Machine Learning. And recently when we premiered, we sort of offered the world's first comprehensive data governance solution in Azure Purview. I would humbly submit it to you that we are leading the way and we're essentially showing how the future of data, AI and the Edge should work in the cloud. >> Yeah, I'd be disappointed if you capitulated in any way, JG. So, thank you for that. And that's kind of last question is looking forward and how you're thinking about the future of cloud. Last decade, a lot about cloud migration, simplifying infrastructure to management and deployment. SaaSifying My Enterprise, a lot of simplification and cost savings and of course redeployment of resources toward digital transformation, other valuable activities. How do you think this coming decade will be defined? Will it be sort of more of the same or is there something else out there? >> I think that the coming decade will be one where customers start to unlock outsize value out of this. What happened to the last decade where people laid the foundation? And people essentially looked at the world and said, "Look, we've got to make a move. "They're largely hybrid, but you're going to start making "steps to basically digitize and modernize our platforms. I will tell you that with the amount of data that people are moving to the cloud, just as an example, you're going to see use of analytics, AI or business outcomes explode. You're also going to see a huge sort of focus on things like governance. People need to know where the data is, what the data catalog continues, how to govern it, how to trust this data and given all of the privacy and compliance regulations out there essentially their compliance posture. So I think the unlocking of outcomes versus simply, Hey, I've saved money. Second, really putting this comprehensive sort of governance regime in place and then finally security and trust. It's going to be more paramount than ever before. >> Yeah, nobody's going to use the data if they don't trust it, I'm glad you brought up security. It's a topic that is at number one on the CIO list. JG, great conversation. Obviously the strategy is working and thanks so much for participating in Cube on Cloud. >> Thank you, thank you, Dave and I appreciate it and thank you to everybody who's tuning into today. >> All right then keep it right there, I'll be back with our next guest right after this short break.

>> Commentator: Live from Orlando, Florida It's theCUBE. Covering Microsoft Ignite. Brought to you by Cohesity. >> Hello everyone and welcome back to theCUBE's live coverage of Microsoft Ignite 2019. We are theCUBE, we are here at the Cohesity booth in the middle of the show floor at the Orange County Convention Center. 26,000 people from around the globe here. It's a very exciting show. I'm your host, Rebecca Knight, along with my co-host, Stu Miniman. We are joined by Francesca Lazzeri. She is a Ph.D Machine Learning Scientist and Cloud Advocate at Microsoft. Thank you so much for coming on the show. >> Thank you for having me. I'm very excited to be here. >> Rebecca: Direct from Cambridge, so we're an all Boston table here. >> Exactly. >> I love it. I love it. >> We are in the most technology cluster, I think, in the world probably. >> So two words we're hearing a lot of here at the show, machine learning, deep learning, can you describe, define them for us here, and tell us the difference between machine learning and deep learning. >> Yeah, this is a great question and I have to say a lot of my customers ask me this question very, very often. Because I think right now there are many different terms such as deep learning as you said, machine learning, AI, that have been used more or less in the same way, but they are not really the same thing. So machine learning is portfolio, I would say, of algorithms, and when you say algorithms I mean really statistical models, that you can use to run some data analysis. So you can use these algorithms on your data, and these are going to produce what we call an output. Output are the results. So deep learning is just a type of machine learning, that has a different structure. We call it deep learning because there are many different layers, in a neural network, which is again a type of machine learning algorithm. And it's very interesting because it doesn't look at the linear relation within the different variables, but it looks at different ways to train itself, and learn something. So you have to think just about deep learning as a type of machine learning and then we have AI. AI is just on top of everything, AI is a way of building application on top of machine learning models and they run on top of machine learning algorithms. So it's a way, AI, of consuming intelligent models. >> Yeah, so Francesca, I know we're going to be talking to Jeffrey Stover tomorrow about a topic, responsible AI. Can you talk a little bit about how Microsoft is making sure that unintentional biases or challenges with data, leave the machine learning to do things, or have biases that we wouldn't want to otherwise. >> Yes, I think that Microsoft is actually investing a lot in responsible AI. Because I have to say, as a data scientist, as a machine learning scientist, I think that it's very important to understand what the model is doing and why it's give me analysis of a specific result. So, in my team, we have a tool kit, which is called, interpretability toolkit, and it's really a way to unpack machine learning models, so it's a way of opening machine learning models and understand what are the different relations between the different viables, the different data points, so it's an easy way through different type of this relation, that you can understand why your model is giving you specific results. So that you get that visibility, as a data scientist, but also as a final consumer, final users of these AI application. And I think that visibility is the most important thing to prevent unbias, sorry, bias application, and to make sure that our results are fair, for everybody. So there are some technical tools that we can use for sure. I can tell you, as a data scientist, that bias and unfairness starts with the data. You have to make sure that the data is representative enough of the population that you are targeting with your AI applications. But this sometimes is not possible. That's why it's important to create some services, some toolkits, that are going to allow you, again, as a data scientist, as a user, to understand what the AI application, or the machine learning model is doing. >> So what's the solution? If the problem, if the root of the problem is the data in the first place, how do we fix this? Because this is such an important issue in technology today. >> Yes, and so there are a few ways that you can use... So first of all I want to say that it's not a issue that you can really fix. I would say that, again, as a data scientist, there are a few things that you can do, in order to check that your AI application is doing a good job, in terms of fairness, again. And so these few steps are, as you said, the data. So most of the time, people, or customers, they just use their own data. Something that is very helpful is also looking at external type of data, and also make sure that, again, as I said, the pure data is representative enough of the entire population. So for example, if you are collecting data from a specific category of people, of a specific age, from a specific geography, you have to make sure that you understand that their results are not general results, are results that the machine learning algorithm learn from that target population. And so it's important again, to look at different type of data, different type of data sets, and use, if you can, also external data. And then, of course, this is just the first step. There's a second step, that you can always make sure that you check your model with a business expert, with data expert. So sometimes we have data scientists that work in siloes, they do not really communicate what they're doing. And I think that this is something that you need to change within your company, within your organization, you have to, always to make sure, that data scientists, machine learning scientists are working closely with data experts, business experts, and everybody's talking. Again, to make sure that we understand what we are doing. >> Okay, there were so many things announced at the show this week. In your space, what are some of the highlights of the things that people should be taking away from Microsoft Ignite. >> So I think that as your machine learning platform has been announcing a lot of updates, I love the product because I think it's a very dynamic product. There is, what we now call, the designer, which is a new version of the old Azure Machine Learning Studio. It's a drag and drop tool so it's a tool that is great for people who do not want to, code to match, or who are just getting started with machine learning. And you can really create end-to-end machine learning pipelines with these tools, in just a matter of a few minutes. The nice thing is that you can also deploy your machine learning models and this is going to create an API for you, and this API can be used by you, or by other developers in your company, to just call the model that you deployed. As I mentioned before, this is really the part where AI is arriving, and it's the part where you create application on top of your models. So this is a great announcement and we also created a algorithm cheat sheet, that is a really nice map that you can use to understand, based on your question, based on your data, what's the best machine learning algorithm, what's the best designer module that you can use to be build your end-to-end machine learning solution. So this, I would say, is my highlight. And then of course, in terms of Azure Machine Learning, there are other updates. We have the Azure Machine Learning python SDK, which is more for pro data scientists, who wants to create customized models, so models that they have to build from scratch. And for them it's very easy, because it's a python-based environment, where they can just build their models, train it, test it, deploy it. So when I say it's a very dynamic and flexible tool because it's really a tool on the pla- on the Cloud, that is targeting more business people, data analysts, but also pro data scientists and AI developers, so this is great to see and I'm very, very excited for that. >> So in addition to your work as a Cloud advocate at Microsoft, you are also a mentor to research and post-doc students at the Massachusetts Institute of Technology, MIT, so tell us a little more about that work in terms of what kind of mentorship do you provide and what your impressions are of this young generation, a young generation of scientists that's now coming up. >> Yes. So that's another wonderful question because one of the main goal of my team is actually working with a academic type of audience, and we started this about a year ago. So we are, again, a team of Cloud advocates, developers, data scientists, and we do not want to work only with big enterprises, but we want to work with academic type of institutions. So when I say academics, of course I mean, some of the best universities, like I've been working a lot with MIT in Cambridge, Massachusetts Institute of Technology, Harvard, and also now I've been working with the Columbia University, in New York. And with all of them, I work with both the PhD and post-doc students, and most of the time, what I try to help them with is changing their mindset. Because these are all brilliant students, that need just to understand how they can translate what they have learned doing their years of study, and also their technical skillset, in to the real world. And when I say the real world, I mean more like, building applications. So there is this sort of skill transfer that needs to be done and again, working with these brilliant people, I have to say, something that is easy to do, because sometimes they just need to work on a specific project that I create for them, so I give data to them and then we work together in a sort of lab environment, and we build end-to-end solutions. But from a knowledge perspective, from a, I would say, technical perspective, these are all excellent students, so it's really, I find myself in a position in which I'm mentoring them, I prepare them for their industry, because most of them, they want to become data scientist, machine learning scientist, but I have to say that I also learn a lot from them, because at the end of the day, when we build these solutions, it's really a way to build something, a project, an app together, and then we also see, the beauty of this is also that we also see how other people are using that to build something even better. So it's an amazing experience, and I feel very lucky that I'm in Cambridge, where, as you know, we have the best schools. >> Francesca, you've dug in some really interesting things, I'd love to get just a little bit, if you can share, about how machine learning is helping drive competitiveness and innovation in companies today, and any tips you have for companies, and how they can get involved even more. >> Yeah, absolutely. So I think that everything really start with the business problem because I think that, as we started this conversation, we were mentioning words such as deep learning, machine learning, AI, so it's, a lot of companies, they just want to do this because they think that they're missing something. So my first suggestion for them is really trying to understand what's the business question that they have, if there is a business problem that they can solve, if there is an operation that they can improve, so these are all interesting questions that they can ask themselves their themes. And then as soon as they have this question in mind, the second step is understand that, if they have the data, the right data, that are needed to support this process, that is going to help them with the business question. So after that, you understand that the data, I mean, if you understand, if you have the right data, they are the steppings, of course you have to understand if you have also external data, and if you have enough data, as we were saying, because this is very, very important as a first step, in your machine learning journey. And you know, it's important also, to be able to translate the business question in to a machine learning question. Like, for example, in the supervised learning, which is an area of machine learning, we have what is called the regression. Regression is a great type of model, that is great for, to answer questions such as, how many, how much? So if you are a retailer and you wanted to predict how much, how many sales of a specific product you're going to have in the next two weeks, so for example, the regression model, is going to be a good first find, first step for you to start your machine learning journey. So the translation of the business problem into a machine learning question, so it's a consequence in to a machine learning algorithm, is also very important. And then finally, I would say that you always have to make sure that you are able to deploy this machine learning model so that your environment is ready for the deployment and what we call the operizational part. Because this is really the moment in which we are going to allow the other people, meaning internal stake holders, other things in your company, to consume the machine learning model. That's the moment really in which you are going to add business value to your machine learning solution. So yeah, my suggestion for companies who want to start this journey is really to make sure that they have cleared these steps, because I think that if they have cleared these steps, then their team, their developers, their data scientists, are going to work together to build these end-to-end solutions. >> Francesca Lenzetti, thank you so much for coming on theCUBE, it was a pleasure having you. >> Thank you. Thank you. >> I'm Rebecca Knight, Stu Miniman. Stay tuned for more of theCUBE's live coverage of Microsoft Ignite. (upbeat music)