Course curriculum

    1. Learning objectives

    2. Your instructor

    3. What is CI/CD for ML?

    4. Using W&B in your CI/CD workflow

    5. Create a W&B project

    6. Course prerequisites

    7. Before we begin...

    1. Hello World GitHub Action

    2. Python scripts with Github Actions

    3. GithHub Actions secrets

    4. Event triggers

    5. Setting up GitHub Actions environment

    6. Special variables

    7. Using variables inside GitHub Actions

    8. Branch protection rules

    9. Test your learning

    1. Testing ML code introduction

    2. Testing ML code walkthrough

    3. Testing ML code assignment

    4. Test your learning

    1. GitHub API and Actions

    2. GitHub API

    3. Using ghapi in GitHub Actions

    4. Using octokit/rest.js client

    5. GitHub API assignment

    6. Assignment solution

    7. Test your learning

    1. Using W&B API

    2. Promoting a model to the registry

    3. W&B GitHub Action assignment

    4. Project assignment

    5. Assignment solution

    6. Branch protection with W&B API

    7. Deployment with GitOps

    8. Test your learning

    1. Final summary

    2. More resources for you

    3. Before you go...

About this course

  • Free
  • 38 lessons
  • 5 hours of video content

Watch Course Trailer

Your Goals

Sign up for this free Weights & Biases course to:

  • Automate ML pipelines with GitHub Actions

  • Automate testing and evaluation of your ML models

  • Deploy models with confidence

What you'll learn

  • Automate ML pipelines with GitHub Actions

  • Automate testing for your ML code

  • Implement branch protection rules

  • Integrate Github API in your actions

  • Integrate W&B API and programmatic reports into ML workflows

  • Promote models to the registry with W&B


Hamel Husain

Machine Learning Engineer

Hamel Husain is currently an entrepreneur in residence at, where he builds tools for data scientists and machine learning engineers. Hamel has previously worked at Airbnb, DataRobot, and GitHub where he built a wide array of machine learning products and infrastructure. Hamel has contributed to data and infrastructure tools in open source such as Metaflow, Kubeflow, Jupyter, and Great Expectations. Hamel was also a consultant for over 10 years, and used data science to improve business outcomes in the restaurant, entertainment, telecommunications, and retail industries.

Get your MLOps - CI/CD Certificate from W&B