Manage the ML lifecycle with MLflow 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 manage the machine learning lifecycle using MLflow on SUSE Linux Enterprise Server (SLES) Arm64, track experiments, version models, and deploy models as scalable APIs.

What will you learn?

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

• Install and configure MLflow on Google Cloud C4A Axion processors for Arm64
• Track experiments, log metrics, and compare runs using MLflow Tracking
• Manage and version models using the MLflow Model Registry
• Deploy models as APIs and validate end-to-end ML 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