Foundation models make for fantastic demos, but in practice, they can be challenging to put into production. These models work well over datasets that match common training distributions (e.g., generating WEBTEXT or internet images), but may fail on domain-specific tasks or long-tail edge case; the settings that matter most to organizations building differentiated products. We propose a data-centric development approach that organizations can use to adapt foundation models to their own private/proprietary datasets.

We'll describe several techniques, including supervision "warmstarts" and interactive prompting (spoiler alert: no code needed). To make these techniques come to life, we'll walk through real case studies describing how we've seen data-centric development drive AI-powered products, from "AI assist" use cases (e.g., copywriting assistants) to "fully automated" solutions (e.g., loan processing engines).

Talk by: Vincent Chen

Here’s more to explore: LLM Compact Guide: https://dbricks.co/43WuQyb Big Book of MLOps: https://dbricks.co/3r0Pqiz

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