Fueling innovative software
July 15-18, 2019
Portland, OR

Kubernetes for machine learning: Productivity over primitives

Sophie Watson (Red Hat), William Benton (Red Hat)
11:45am–12:30pm Tuesday, July 16, 2019
ML Ops Day
 Location: E145/146
Average rating: ****.
(4.67, 3 ratings)
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Who is this presentation for?

  • Data scientists and machine learning engineers
  • DevOps or infrastructure engineers who support data scientists and machine learning engineers

Level

Intermediate

Description

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.

Prerequisite knowledge

  • Familiarity with machine learning workflows
  • Familiarity with Kubernetes (useful but not required)

What you'll learn

  • Gain a better understanding of what Kubernetes brings to production machine learning workflows and inspiration for how to get started
  • Learn the next steps to get started enabling diverse teams to put intelligence into production on Kubernetes
Photo of Sophie Watson

Sophie Watson

Red Hat

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.

Photo of William Benton

William Benton

Red Hat

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.