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MLOps with Databricks // Maria Vechtomova // #314

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Overcoming ML Challenges with Databricks

This chapter explores the transition challenges data scientists face when moving from notebooks to production-ready machine learning models, with a focus on the capabilities of Databricks. It emphasizes the platform's user-friendly features, the significance of proper model packaging, and highlights the limitations of current feature engineering practices. Through discussions on versioning and workflow management, the chapter illustrates how Databricks addresses common frustrations while enhancing the overall ML development process.

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