eBook
Data-centric MLOps and LLMOps Explore generative AI, RAG and more
The Big Book of MLOps: Second Edition
Discover the latest strategies for deploying generative AI and machine learning models efficiently. The Big Book of MLOps covers how to collaborate on a common platform using powerful, open frameworks such as Delta Lake for data pipelines, MLflow for model management (including LLMs) and Databricks Workflows for automation.
This updated edition will share how building your AI foundation on top of your data platform makes for robust governance and lineage for your data and AI assets. We’ll also dive deeper into architectures for deploying and managing large language models — known as LLMOps.
The problem gets bigger when your data assets exist across multiple clouds with different access management
solutions. What you need is a unified approach to simplify governance for all your data on any cloud.
In this eBook, you’ll learn:
The essential components of an MLOps reference architecture
How to apply retrieval augmented generation (RAG) to enhance language models for more informed and accurate responses
How to leverage a data-centric platform to securely move AI assets into production and govern them
How to monitor data and models through the complete AI lifecycle
Best practices to guide your MLOps planning and decision-making
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