Leveraging Kubernetes for Machine Learning


Duration: 180 mins
Jonathan Johnson
Independent Software Architect

With the advent of containers, Kubernetes evolved as the defacto orchestration solution to coordinate hundreds of containers at scale across a datacenter. Kubernetes opens the door for developers to access the benefits of distributed computing. As compute capacity increases relative to price we have an explosion of Machine Learning applications moving to Kubernetes. Does anybody remember “Wonder Twin powers, activate!”

You will learn how Kubernetes offers to Machine Learning an ideal orchestration tool for hosting your clever applications. We will look at common practices, containers, and deployment architectures that are common for cloud native Machine Learning. Kubeflow is one of the dominating solutions, but there are others.

  • Architectural understanding of Kubernetes from a developer perspective
  • Deploying and monitoring application on Kubernetes
  • Understanding the architecture of Kubeflow

Hands-on exercises:

  • Best practices for containerizing Python applications
  • Scaling stateful Python applications
  • Using Kubeflow
  • Leverage ML performant hardware
  • Writing, Deploying, Running and Monitoring some Machine Learning solutions

Prerequisites:

  • Just a laptop with wi-fi and a browser
  • Machine learning fundamentals
  • Basic knowledge of Python and writing containers

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