Course curriculum

    1. Welcome to this course

    1. Introduction to Ray AI Libraries

    2. Overview of Ray AI Libraries

    3. Discussing an end-to-end example

    4. Running an experiment with AI Libraries

    5. End-to-end example code

    6. Chapter Notebook

    7. Ray AI Libraries Quiz

    1. Introduction to Ray Train

    2. PyTorch introductory example

    3. Overview of Distributed Training with Ray Train

    4. Overview of the training loop in Ray Train

    5. Migrating the model and dataset to Ray Train

    6. Reporting metrics and checkpoints

    7. Launching the distributed training job

    8. Accessing training results

    9. Chapter Notebook

    10. Ray Train Quiz

    1. Introduction to Ray Tune

    2. Loading and visualizing data

    3. Setting up a PyTorch model

    4. Ray Tune Basics

    5. Diving deeper into Ray Tune concepts

    6. Hyperparameter tuning the PyTorch model using Ray Tune

    7. Chapter Notebook

    8. Ray Tune Quiz

    1. Introduction to Ray Data

    2. When to use Ray Data

    3. Loading Data

    4. Transforming Data

    5. Data Operations: Grouping, Aggregation, and Shuffling

    6. Persisting Data

    7. Chapter Notebook

    8. Ray Data Quiz

    1. Introduction to Ray Serve

    2. Overview of Ray Serve

    3. Implement a Classifier service

    4. Advanced features of Ray Serve

    5. Ray Serve in Production

    6. Chapter Notebook

    7. Ray Serve Quiz

About this course

  • 42 lessons
  • 3 hours of video content
  • Level: Beginner
  • Ray AI Libraries Overview
  • Featuring XGBoost and PyTorch