Second Edition dives into the latest strategies for efficiently deploying generative AI and machine learning models. This comprehensive guide explores how to harness powerful, open frameworks like Delta Lake for data pipelines, MLflow for model management (including large language models), and Databricks Workflows for automation. It emphasizes the importance of building your AI foundation on a robust data platform to ensure effective governance and lineage of your data and AI assets.
The updated edition provides detailed insights into the essential components of an MLOps reference architecture and the application of retrieval-augmented generation (RAG) to enhance language models for more informed responses. You’ll also learn how to leverage a data-centric platform to securely transition AI assets into production and maintain comprehensive oversight through the entire AI lifecycle. Additionally, it covers best practices to inform your MLOps planning and decision-making.
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