A01=Maria Vechtomova
Author_Maria Vechtomova
Category=UYQF
databricks
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eq_isMigrated=2
eq_nobargain
forthcoming
machine learning
MLops
Product details
- ISBN 9798341608252
- Dimensions: 178 x 232mm
- Publication Date: 31 Aug 2026
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
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MLOps engineers have to deal with a glut of tools and SaaS applications, not to mention technical debt clogging the system. Such complexity requires a comprehensive approach. The Databricks Platform provides all the critical components for end-to-end MLOps and LLMOps in one place. This exhaustive book shows you how to use Databricks to build and manage a robust ML system that delivers on your business's needs.
Maria Vechtomova guides you through MLOps principles and explains how Databricks handles the machine learning lifecycle holistically, from data preparation to model deployment and monitoring, and enables data engineers, data scientists, and MLOps engineers to collaborate seamlessly. To put all the pieces together, you'll navigate two ML projects: a real-time ML application and a RAG system that highlights LLM-specific Databricks features.
Understand the Databricks components for MLOps
Unpack ML model serving architectures
Track your machine learning experiments and register your models
Build an ML application that uses feature and model serving, and model serving with automatic feature lookup
Deploy a real-time ML application and a RAG application
Use Databricks to monitor ML applications for data and model drift
Maria Vechtomova guides you through MLOps principles and explains how Databricks handles the machine learning lifecycle holistically, from data preparation to model deployment and monitoring, and enables data engineers, data scientists, and MLOps engineers to collaborate seamlessly. To put all the pieces together, you'll navigate two ML projects: a real-time ML application and a RAG system that highlights LLM-specific Databricks features.
Understand the Databricks components for MLOps
Unpack ML model serving architectures
Track your machine learning experiments and register your models
Build an ML application that uses feature and model serving, and model serving with automatic feature lookup
Deploy a real-time ML application and a RAG application
Use Databricks to monitor ML applications for data and model drift
