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.
In this eBook, you’ll learn:
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.
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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