Deployed: The AI Product Podcast
Insights from Building AI Systems At Google Scale: In Conversation With Kyle Nesbit
Dec 10, 2024
Kyle Nesbit, a longtime Googler with 17 years of experience in AI and distributed systems, shares invaluable insights about building AI at scale. He discusses the challenges of transitioning traditional engineering teams to embrace LLM development and underscores the importance of starting with solid evaluation metrics. Kyle reveals strategies for iterative improvement, tackling data discovery issues, and balancing product quality during scaling. He also provides a peek into the real story behind AI demos and the complexities of integration within organizations.
52:45
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Quick takeaways
- Transitioning teams to AI-focused workflows requires a careful blend of iterative feedback loops and a deep understanding of evaluation metrics.
- The advent of transformer models has simplified LLM development, but a renewed focus on fundamental ML principles is still necessary.
Deep dives
The Importance of Iterative Problem Framing
Addressing complex problems requires a continuous iterative approach rather than a one-time solution. As users interact with products, feedback and metrics must be regularly reviewed to identify gaps in quality and engagement. This iterative process helps refine metrics and improve data collection, ensuring that the product evolves with user needs. Adapting to new signals from users is crucial for maintaining high standards in product offerings.
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