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Evaluating and Integrating ML Models // Morgan McGuire and Anish Shah // #213

Feb 21, 2024
Morgan McGuire and Anish Shah discuss the challenges of productionizing large language models, including cost optimization, latency requirements, trust of output, and debugging. They also mention an upcoming AI in Production Conference on February 22 with informative workshops.
51:56

Podcast summary created with Snipd AI

Quick takeaways

  • User data is crucial for evaluating ML models, aiding in identifying areas for improvement and enhancing performance.
  • Evaluation challenges include benchmark limitations, gaming leaderboards, and the need for rigorous assessment of model strengths and weaknesses.

Deep dives

Importance of Gathering User Data for Evaluation

One key insight from the podcast is the importance of gathering user data for evaluation purposes. The hosts emphasize the need to mine user data and incorporate it into the evaluation framework. By collecting data from actual users of an application that utilizes LM (large language model), teams can gather insights on how users interact with the application and identify areas for improvement. Additionally, this user data can inform decisions on updating documentation, improving product intuitiveness, and addressing specific use cases. It is seen as a valuable resource for evaluating and enhancing the performance of LM-based applications.

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