How to Get Out of AI Proof-of-Concept Purgatory with Hugo Bowne-Anderson
Catalog & Cocktails: The Honest, No-BS Data Podcast
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Navigating AI Development: From Data to Insights
This chapter focuses on the essential distinctions between the 'what' and 'how' of AI applications, advocating for a data-centric approach over a model-centric one. It covers the evolution of hyperparameter tuning, the importance of problem-specific decisions in DevOps, and the integration of large language models with business logic, highlighting challenges like hallucinations. The dialogue emphasizes continuous learning and practical evaluation methods while stressing the relevance of foundational practices amid the advancements in AI technology.
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