Implementing MLOps on GCP

Implementing MLOps on GCP

Introduction

In this hack, you’ll implement the full lifecycle of an ML project. We’ll provide you with a sample code base and you’ll work on automating continuous integration (CI), continuous delivery (CD), and continuous training (CT) for a machine learning (ML) system.

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Learning Objectives

This hack will help you explore the following tasks:

• Using Cloud Source Repositories for version control
• Using Cloud Build for automating continuous integration and delivery
• Vertex AI for
  • Exploration through an interactive environment
  • Training on diverse hardware
  • Model registration
  • Managed pipelines
  • Model serving
  • Model monitoring

The instructions are minimal, meaning that you need to figure out things :) That’s by design

Challenges

• Setting up the environment
  • Before we can hack, you will need to set up a few things.
  • Run the instructions on our Environment Setup page.
• Challenge 1: Let’s start exploring!
• Challenge 2: If it isn’t in version control, it doesn’t exist
• Challenge 3: You break the build, you buy cake
• Challenge 4: Automagic training with pipelines
• Challenge 5: Make it work and make it scale
• Challenge 6: Monitor your models
• Challenge 7: Close the loop

Prerequisites

• Knowledge of Python
• Knowledge of Git
• Basic knowledge of GCP
• Access to a GCP environment

Contributors

• Murat Eken