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Ray AI Libraries provide a high-level, easy-to-use set of APIs for the full AI/ML lifecycle, designed with MLEs and SWEs in mind. Consume all sorts of data, train models, tune them, serve them, and use them for batch inference or semantic featurization (like embeddings) -- all with minimal code, using patterns you already know. If you'd like to simplify a large-scale workflow, this session is for you.
In this workshop, we'll introduce patterns and APIs for using the Ray AI Libraries, including Ray Data, Ray Train, and Ray Serve. We'll use running code examples that demonstrate end-to-end workflows with Ray and show examples featuring both tabular data modeling (e.g., XGBoost) and deep learning models (PyTorch).
By the end of the class, you'll know when and how to replace local, small-scale, or heterogeneous data pipelines with the unified Ray AI Libraries. You'll have working notebooks to use later. And you'll gain experience using Ray to deliver the fastest performance and the largest scales required by today's projects.
Prerequisites:
This is beginner friendly training, you will learn about Ray fundamentals.
Familiarity with basic ML concepts and workflows.
Intermediate-level experience with Python.
Ray Libraries:
Ray Data
Ray Train
Ray Serve
In this workshop, we'll introduce patterns and APIs for using the Ray AI Libraries, including Ray Data, Ray Train, and Ray Serve. We'll use running code examples that demonstrate end-to-end workflows with Ray and show examples featuring both tabular data modeling (e.g., XGBoost) and deep learning models (PyTorch).
By the end of the class, you'll know when and how to replace local, small-scale, or heterogeneous data pipelines with the unified Ray AI Libraries. You'll have working notebooks to use later. And you'll gain experience using Ray to deliver the fastest performance and the largest scales required by today's projects.
Prerequisites:
This is beginner friendly training, you will learn about Ray fundamentals.
Familiarity with basic ML concepts and workflows.
Intermediate-level experience with Python.
Ray Libraries:
Ray Data
Ray Train
Ray Serve
