Building Machine Learning Systems with a Feature Store : Batch, Real-Time, and LLM Systems
Language: English
Published by O'reilly Media Dez 2025, 2025
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 87.48
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Add to basketItem description from seller
Neuware - Get up to speed on a new unified approach to building machine learning (ML) systems with a feature store. Using this practical book, data scientists and ML engineers will learn in detail how to develop and operate batch, real-time, and agentic ML systems. Author Jim Dowling introduces fundamental principles and practices for developing, testing, and operating ML and AI systems at scale. You'll see how any AI system can be decomposed into independent feature, training, and inference pipelines connected by a shared data layer. Through example ML systems, you'll tackle the hardest part of ML systems--the data, learning how to transform data into features and embeddings, and how to design a data model for AI. - Develop batch ML systems at any scale - Develop real-time ML systems by shifting left or shifting right feature computation - Develop agentic ML systems that use LLMs, tools, and retrieval-augmented generation - Understand and apply MLOps principles when developing and operating ML systems.…
Seller Inventory # 9781098165239
- Title
- Building Machine Learning Systems with a Feature Store : Batch, Real-Time, and LLM Systems
- Author
- Jim Dowling
- Publisher
- O'reilly Media Dez 2025
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1098165233
- ISBN 13
- 9781098165239
- Item weight
- 792 grams
- Dimensions
- 234x178x27 mm
Get up to speed on a new unified approach to building machine learning (ML) systems with a feature store. Using this practical book, data scientists and ML engineers will learn in detail how to develop and operate batch, real-time, and agentic ML systems.
Author Jim Dowling introduces fundamental principles and practices for developing, testing, and operating ML and AI systems at scale. You'll see how any AI system can be decomposed into independent feature, training, and inference pipelines connected by a shared data layer. Through example ML systems, you'll tackle the hardest part of ML systems--the data, learning how to transform data into features and embeddings, and how to design a data model for AI.
-
Develop batch ML systems at any scale
-
Develop real-time ML systems by shifting left or shifting right feature computation
-
Develop agentic ML systems that use LLMs, tools, and retrieval-augmented generation
-
Understand and apply MLOps principles when developing and operating ML systems
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