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

Streamline your ML workflows and save valuable time by automating your pipelines and deploying models with confidence. Learn how to use GitHub Actions and integrate W&B experiment tracking in this practical, hands-on learning experience.

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

Instructor

Hamel Husain

Founder @ Parlance Labs

Hamel is currently a founder at Parlance Labs, a research lab and consultancy focused on LLMs. Previously he was an entrepreneur in residence at fast.ai, where he built tools for data scientists and machine learning engineers. He also 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.

Reviews

5 star rating

Insightful and concisely explained course

Simran Simran

I have found the course really informative and helpful. With this course, I came to understand the concepts behind CI/CD, GitHub action workflows and how to leverage them for MLOps effectively. The explanation was also comprehensive and clear. I'd...

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I have found the course really informative and helpful. With this course, I came to understand the concepts behind CI/CD, GitHub action workflows and how to leverage them for MLOps effectively. The explanation was also comprehensive and clear. I'd like to thank the course instructor for the same. With this course, I learnt about the paradigms of GitOps, wandb api and code testing which I'm sure will help in building and deploying ML pipelines and tracking experimentations. Looking forward to more such intriguing courses on wandb platform!

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5 star rating

Amazing course

Raj Bhalwankar

amazing course with good practical examples and exercises. Just wish the exercises were in a in-build IDE environment

amazing course with good practical examples and exercises. Just wish the exercises were in a in-build IDE environment

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5 star rating

Great content

Ama Ama

Really great content and worth going over it twice to solidify concepts

Really great content and worth going over it twice to solidify concepts

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