Keynote Talk: Introduction to Distributed ML workloads with Ray on Kubernetes

The rapidly evolving landscape of Machine Learning and Large Language Models demands efficient scalable ways to run distributed workloads to train, fine-tune and serve models. Ray is an Open Source framework that simplifies distributed machine learning, and Kubernetes streamlines deployment.
In this introductory talk, we’ll uncover how to combine Ray and Kubernetes for your ML projects.
You will learn about:

  • – Basic Ray concepts (actors, tasks) and their relevance to ML
  • – Setting up a simple Ray cluster within Kubernetes
  • – Running your first distributed ML training job

February 29 ,2024 March 11 ,2024

Abstract submission deadline

March 7 ,2024 March 18 ,2024

Paper submission deadline

April 22 ,2024

Accept/Reject notification

May 12 ,2024

Camera ready copy due

May 27-28 ,2024

Metis Spring school

May 29-31 ,2024

Netys Conference


Revised selected papers will be published as a post-proceedings in Springer's LNCS "Lecture Notes in Computer Science"

Partners & Sponsors