Scale AI workloads with Ray on Google Cloud C4A Axion VM

About this Learning Path

Who is this for?

This is an introductory topic for DevOps engineers, ML engineers, and software developers who want to deploy and run distributed workloads using Ray on SUSE Linux Enterprise Server (SLES) Arm64, execute parallel tasks, perform hyperparameter tuning, and serve models at scale.

What will you learn?

Upon completion of this Learning Path, you will be able to:

• Install and configure Ray on Google Cloud C4A Axion processors for Arm64
• Run distributed tasks and parallel workloads using Ray Core
• Perform distributed training and hyperparameter tuning using Ray Train and Ray Tune
• Deploy scalable APIs using Ray Serve and validate end-to-end execution

Prerequisites

Before starting, you will need the following:

• A Google Cloud Platform (GCP) account with billing enabled
• Basic familiarity with Python and distributed systems concepts