We've all done RAG, now what?
Longtime friend of the show Rajiv Shah returns to unpack lessons from a year of building retrieval-augmented generation (RAG) pipelines and reasoning models integrations. We dive into why so many AI pilots stumble, why evaluation and error analysis remain essential data science skills, and why not every enterprise challenge calls for a large language model.
Featuring:
Rajiv Shah – LinkedIn
Daniel Whitenack – Website, GitHub, X
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When data leakage turns into a flood of trouble
Rajiv Shah teaches Daniel and Chris about data leakage, and its major impact upon machine learning models. It’s the kind of topic that we don’t often think about, but which can ruin our results. Raj discusses how to use activation maps and image embedding to find leakage, so that leaking information in our test set does not find its way into our training set.
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Featuring:
Rajiv Shah – Website, GitHub, LinkedIn, X
Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X
Show Notes:
Rajiv Shah | University of Illinois at Chicago
Rajiv Shah | DataRobot Blog
DataRobot
Upcoming Events:
Register for upcoming webinars here!