Train and deploy XGBoost models on Google Cloud C4A Axion VM

About this Learning Path

Who is this for?

This is an introductory topic for DevOps engineers, ML engineers, data engineers, and software developers who want to train and deploy XGBoost machine learning models on SUSE Linux Enterprise Server (SLES) Arm64, optimize model performance, benchmark training workloads, and expose models through scalable inference APIs.

What will you learn?

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

• Install and configure XGBoost on Google Cloud C4A Axion processors for Arm64
• Train and evaluate machine learning models using XGBoost
• Tune model hyperparameters and benchmark large-scale datasets
• Deploy trained XGBoost models as REST APIs and validate inference workflows

Prerequisites

Before starting, you will need the following:

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