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Ep 47: Chief AI Scientist of Databricks Jonathan Frankle on Why New Model Architectures are Unlikely, When to Pre-Train or Fine Tune, and Hopes for Future AI Policy

Unsupervised Learning

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Harnessing AI with Databricks Tools

This chapter discusses the practical applications of Databricks tools like Spark, Unity Catalog, and MLflow in building machine learning models, significantly reducing data processing time. It emphasizes the importance of product-market fit for AI applications, analyzing scenarios where customized models offer advantages. The conversation addresses the complexities of AI ethics and explainability, exploring the evolving landscape of AI infrastructure and the role of strategic acquisitions in enhancing platform capabilities.

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