Name: Enhancing Foundation Models with Your Data
Start: 2024-04-17T18:30:00+01:00
End: 2024-04-17T20:30:00+01:00
Location: Workday - Dublin

This is a Practical Workshop on Amazon Bedrock and RAG (Retrieval-augmented Generation).

Should I use an existing foundation model (FM) or train a new model? Often, the answer will be 'use an existing FM' because these models are so capable and rapidly evolving in their generalized knowledge and capabilities. Plus, you can customize either the responses of the model or the model itself with your own enterprise data. We'll walk through Amazon Bedrock, which offers a choice of high-performing foundation models, and show how you can customize those FMs with your own data. In this hands-on session, I will guide you through how you can use any foundational model and perform pre-training/continuous training using Bedrock and implement Retriever-Answer Generator (RAG) using Aurora as a vector store in Python.

Sukanya Mandal

Harshita Tyagi

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Suman Debnath is a Principal Developer Advocate for Machine Learning and Data at Amazon Web Services. Passionate about deep learning, natural language understanding, and large-scale distributed systems, he is also an avid fan of Python. At AWS, he leads Generative AI and Machine Learning content delivery, with a goal to democratize machine learning engineering and empower developers and enterprises through developer-focused content. He has hosted over 250 events worldwide, including prestigious conferences such as PyCon, the Python Web Conference, and ODSC, as well as meetups in countries like India, the US, Australia, Italy, and the UK. In these events, he has shared insights on cloud computing, machine learning, and data engineering.

Suman Debnath, AWS

Enhancing Foundation Models with Your Data

Workday - Dublin

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Enhancing Foundation Models with Your Data

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