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 software engineer in an Emerging Technology Group at Red Hat, where she applies her data science and statistics skills to solving business problems and informing next-generation infrastructure for intelligent application development. She has a background in mathematics and holds a PhD in Bayesian statistics, in which she developed algorithms to estimate intractable quantities quickly and accurately.
William Benton leads a team of data scientists and engineers at Red Hat, where he has built machine learning systems to solve problems ranging from understanding infrastructure logs at datacenter scale to designing better cycling workouts.
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