Training 4: Introduction to Ray AI Libraries for Deep Learning (3 hours)
9:00 AM - 12:00 PM
Level: Beginner
Hands-on Lab
Software Engineer, ML Practitioner, ML Engineer, Data Scientist
Ray Data, Train, Tune, ServeIn this beginner friendly course, you will learn about the Ray project and how it provides a unified compute layer for scaling deep learning applications. From data loading to model training and hyperparameter tuning to prediction and serving, we will cover how to scale each stage of your ML model lifecycle.
You will explore Ray AI Libraries that enable you to build end-to-end machine learning applications. To illustrate this, you will work with image classification, a widely recognized deep learning use case.
By the conclusion of this tutorial, you will feel confident in harnessing Ray's full potential for your own machine learning projects.
Learning Outcomes
- Understand common challenges and trade-offs when scaling ML pipelines.
- Distribute end-to-end deep learning workloads using Ray AI Libraries.
- Implement and extend examples presented to scale deep learning model training, tuning and serving.
Prerequisites
- Familiarity with basic ML concepts and workflows.
- Intermediate-level experience with Python.
- No prior experience with Ray or distributed computing required.
