WEBINAR "Demo Tutorial: Customizable RAG workflows with your own Data"

To access this webinar, please register here: https://hubs.li/Q02kMnzS0

Topic: "Demo Tutorial: Customizable RAG workflows with your own Data"

Speaker: Christy Bergman, AI/ML Developer Advocate at Zilliz

Christy is a passionate Developer Advocate at Zilliz. She previously worked in distributed computing at Anyscale and as a Specialist AI/ML Solutions Architect at AWS. Christy studied applied math, is a self-taught coder, and has published papers, including one with ACM Recsys. She enjoys hiking and bird watching.

Abstract:
You’ve heard good data matters in Machine Learning, but does it matter for Generative AI applications? Corporate data often differs significantly from the general internet data used to train most foundation models. Join us for a Python demo tutorial on building a customizable RAG (Retrieval Augmented Generation) stack using OSS Milvus vector database, LangChain, Ragas, HuggingFace, and optional Zilliz cloud and OpenAI.

Learn best practices and advanced techniques to optimize GenAI workflows with your own data.

What you’ll learn:
* Using Python, learn how to build a customizable open source RAG (Retrieval Augmented Generation) chatbot with Milvus vector database, LangChain, Ragas, and HuggingFace models, and optional Zilliz cloud and OpenAI.
* Best practices around embedding text data ("embedding" in AI is like "featurization" in ML).
* Best practices around vector indexing and search.
* Best practices around RAG evaluation with Ragas.

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