Train and deploy XGBoost models on Google Cloud C4A Axion VM
Introduction
Understand XGBoost and Google Axion C4A for machine learning
Create Google Cloud firewall rules for XGBoost
Create a Google Axion C4A virtual machine for XGBoost
Install XGBoost and train machine learning models
Deploy and access an XGBoost inference API
Next Steps
Train and deploy XGBoost models on Google Cloud C4A Axion VM
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