Building Data Science with Foundation LLM Models

Today, we’re talking about building real AI products with foundation models. Not toy demos, not vibes. We’ll get into the boring dashboards that save launches, evals that change your mind, and the shift from analyst to AI app builder. Our guide is Hugo Bowne-Anderson, educator, podcaster, and data scientist, who’s been in the trenches from scalable Python to LLM apps. If you care about shipping LLM features without burning the house down, stick around.

Episode sponsors

Posit
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Talk Python Courses

Links from the show

Hugo Bowne-Anderson: x.com
Vanishing Gradients Podcast: vanishinggradients.fireside.fm
Fundamentals of Dask: High Performance Data Science Course: training.talkpython.fm
Building LLM Applications for Data Scientists and Software Engineers: maven.com
marimo: a next-generation Python notebook: marimo.io
DevDocs (Offline aggregated docs): devdocs.io
Elgato Stream Deck: elgato.com
Sentry's Seer: talkpython.fm
The End of Programming as We Know It: oreilly.com
LorikeetCX AI Concierge: lorikeetcx.ai
Text to SQL & AI Query Generator: text2sql.ai
Inverse relationship enthusiasm for AI and traditional projects: oreilly.com

Watch this episode on YouTube: youtube.com
Episode #526 deep-dive: talkpython.fm/526
Episode transcripts: talkpython.fm

Theme Song: Developer Rap
🥁 Served in a Flask 🎸: talkpython.fm/flasksong

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