Vanishing Gradients

Episode 32: Building Reliable and Robust ML/AI Pipelines

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Jul 27, 2024
Join Shreya Shankar, a UC Berkeley researcher specializing in human-centered data management systems, as she navigates the exciting world of large language models (LLMs). Discover her insights on the shift from traditional machine learning to LLMs and the importance of data quality over algorithm issues. Shreya shares her innovative SPaDE framework for improving AI evaluations and emphasizes the need for human oversight in AI development. Plus, explore the future of low-code tools and the fascinating concept of 'Habsburg AI' in recursive processes.
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ANECDOTE

AI-Generated Music

  • Shreya Shankar's interest in AI was sparked by an internship at Google.
  • She observed AI-generated music and was inspired to take more AI classes.
INSIGHT

ML Engineering Reality

  • Shreya's industry experience revealed that most ML work involves data engineering.
  • Training models was a small part of her role as an ML engineer.
ADVICE

Data Flywheels

  • Continuously evolve your LLM application based on production data.
  • Label production data, correlate it with human judgment, and use it to improve prompts.
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