This podcast offer a comprehensive look into the principles and practices of MLOps and LLMOps, with a particular focus on security and performance optimization within the Databricks platform. This podcast introduces concepts like Unity Catalog for unified governance and Model Serving for efficient deployment, also covering the unique aspects of managing Large Language Models (LLMs) through prompt engineering, RAG, and fine-tuning. The Databricks blog on LLM inference performance discusses key challenges and optimization techniques, emphasizing the importance of memory bandwidth and batching strategies. Finally, the Databricks AI Security Framework (DASF) outlines a detailed guide to managing risks and implementing security controls across the entire AI lifecycle, applicable to various AI deployment models and integrating with Databricks features like MLflow and Clean Rooms.

MLOps Brief Guide