Today we’re joined by Jordan Edwards, Principal Program Manager for MLOps on Azure ML at Microsoft.
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In our conversation, Jordan details how Azure ML accelerates model lifecycle management with MLOps, which enables data scientists to collaborate with IT teams to increase the pace of model development and deployment. We discuss various problems associated with generalizing ML at scale at Microsoft, and how those problems are prioritized, what exactly is MLOps, what the role of testing is in an MLOps environment, and experiences working with customers to implement these tests. Finally, we explore what are the “four phases” along the journey of customer implementation of MLOps, how companies should look at hiring ML Engineers vs DevOps Engineers, and other aspects of managing model life cycles that Jordan finds important for us to think about.
Thanks to our sponsor!
I’d like to thank Microsoft for their support of the show, and their sponsorship of this series. 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. 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. 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. 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.
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“More On That Later” by Lee Rosevere licensed under CC By 4.0