Kubernetes is today’s hottest way to deploy and manage contemporary applications in the cloud, but it also offers the essential foundation for repeatable and reliable machine learning workflows.
Sophie Watson and William Benton demonstrate open source tools that build on Kubernetes to facilitate solving data science workflow challenges for practitioners. They focus on high-level tools that build productive solutions on powerful primitives without forcing data scientists to care about the primitive details of their infrastructure. They’ll walk you through a typical machine learning workflow and show you how Kubernetes supports data scientists at each step. You’ll see tools that effortlessly provision custom research environments, publish reproducible notebooks, operationalize models and pipelines as services, and detect data drift automatically.
Sophie Watson is a senior data scientist at Red Hat, where she helps customers use machine learning to solve business problems in the hybrid cloud. She’s a frequent public speaker on topics including machine learning workflows on Kubernetes, recommendation engines, and machine learning for search. Sophie earned her PhD in Bayesian statistics.
William Benton is an engineering manager and senior principal software engineer at Red Hat, where he leads a team of data scientists and engineers. He’s applied machine learning to problems ranging from forecasting cloud infrastructure costs to designing better cycling workouts. His focus is investigating the best ways to build and deploy intelligent applications in cloud native environments, but he’s also conducted research and development in the areas of static program analysis, managed language runtimes, logic databases, cluster configuration management, and music technology.
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