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Retrieval-Augmented Generation (RAG) is transforming how AI systems interact with data, offering a powerful approach to enhance information retrieval and generation. This workshop is designed for AI practitioners who want to build and deploy RAG-powered applications from the ground up, leveraging Ray's capabilities for scalability and efficiency.
You'll be guided through the entire lifecycle of a RAG system, starting with constructing and encoding a knowledge base using Ray Data. From there, you'll learn to deploy retrieval and generation endpoints with Ray Serve, ensuring your application can handle real-world queries effectively.
By the end of this training, you will have a comprehensive understanding of the RAG application lifecycle and the skills to design, and deploy RAG systems. Join us to enhance your AI toolkit and master the art of creating scalable, high-quality RAG applications with Ray.
Prerequisites:
This is beginner friendly training, you will learn about RAG from first principles.
Basic understanding of LLMs-enabled use cases.
Intermediate-level experience with Python.
Ray Libraries:
Ray Data
Ray Serve
You'll be guided through the entire lifecycle of a RAG system, starting with constructing and encoding a knowledge base using Ray Data. From there, you'll learn to deploy retrieval and generation endpoints with Ray Serve, ensuring your application can handle real-world queries effectively.
By the end of this training, you will have a comprehensive understanding of the RAG application lifecycle and the skills to design, and deploy RAG systems. Join us to enhance your AI toolkit and master the art of creating scalable, high-quality RAG applications with Ray.
Prerequisites:
This is beginner friendly training, you will learn about RAG from first principles.
Basic understanding of LLMs-enabled use cases.
Intermediate-level experience with Python.
Ray Libraries:
Ray Data
Ray Serve
