![]() Sam Charrington: And what were you working on before Azure AI?Įrez Barak: So before Azure AI, in Microsoft, I worked for a team called Share Data, which really created a set of data platforms for our internal teams. Sort of figuring that out and then really going deep where you landed, right? ‘Cause if we end up building a new SDK, we’re going to spend a whole lot of time with our data science customers, our data science internal teams and figure out, “Well, how should our SDK look like?”īut if you’re building something like Auto ML that’s targeted not only at the deeper data scientist, but also the deeper rooted data professionals, you’re going to spend some time with them and understand not only what they need, but also how that applies to the world of data science. You really have to figure out where most data scientists of all skills need the help, want the help, are looking for tools and products and services on Azure to help them out, and I think that’s the part I find most compelling. So you really have to think between the two worlds and how one empowers the other. If you just think about the engineering perspective, you’ll probably end up with great info that doesn’t align with any of the data science. ![]() I think it’d be remiss to think that if you’re a data science perspective, and you’re trying to build a product and really looking to build the right set of products for people to use as they go through their AI journey, you’d probably miss out on an aspect of it. Or none of the above? Or all of the above?Įrez Barak: I’m actually going to go with all of the above. Sam Charrington: And so do you come at this primarily from a data science perspective, a research perspective, an engineering perspective? So just looking at that end to end and understanding how we enable others to really go through that process in a responsible trusted and known way has been a super exciting journey so far. How do we enable data scientists of all skills to operate through the machine learning lifecycle, starting from the data to the training, to registering the models to putting them in productions and managing them, a process we call ML Ops. I was lucky enough to join the Azure AI group, and there’s really three focal areas within that group. So seeing that opportunity, imagining that potential, has naturally led me to work with AI. The world of opportunity with AI is really only limited by the amount of data you have, the uniqueness of the data you have and the access you have to data.Īnd once you’re able to connect those two worlds, a lot of things like predictions, new insights, new directions, sort of come out of the woodwork. ![]() And I think roughly about five to 10 years ago, it became apparent that the next chapter for anyone working with data has to weave itself through the AI world. But before we dig into that, I’d love to hear how you got started working in ML and AI.Įrez Barak: It’s a great question because I’ve been working with data for quite a while. We will be diving into a topic that is generating a lot of excitement in the industry and that is Auto ML and the automation of the data science process. Sam Charrington: I’m super excited about this conversation. Stay tuned to learn how Microsoft is enabling developers, data scientists and MLOps and DevOps professionals across all skill levels to increase productivity, operationalize models at scale and innovate faster and more responsibly with Azure machine learning.Įrez Barak: Thank you. Microsoft customers like Spotify, Lexmark, and Airbus, choose Azure AI because of its proven enterprise grade capabilities and innovations, wide range of developer tools and services and trusted approach. Millions of developers and data scientists around the world are using Azure AI to build innovative applications and machine learning models for their organizations, including 85% of the Fortune 100. ![]() Thanks to decades of breakthrough research and technology, Microsoft is making AI real for businesses with Azure AI, a set of services that span vision, speech, language processing, custom machine learning, and more. Before we jump in, I’d like to thank Microsoft for their support of the show and their sponsorship of this series. This week on the podcast, I’m excited to share a series of shows recorded in Orlando during the Microsoft Ignite conference. ![]() If you’re at either event, please reach out. Also, this week I’m at re:Invent and next week I’ll be at NeurIPS. It’s not too late to join us, which you can do by visiting /community. A quick reminder that we’ve got a bunch of newly formed or forming study groups, including groups focused on Kaggle competitions and the fast.ai NLP and Deep Learning for Coders part one courses. Sam Charrington: Hey, what’s up everyone? This is Sam. ![]()
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