Building Machine Learning Systems by Dowling Jim (28 results)

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Paperback or Softback. Condition: New. Building Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems. Book.

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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condition: New. Get up to speed on a new unified approach to building machine learning (ML) systems with batch data, real-time data, and large language models (LLMs) based on independent, modular ML pipelines and a shared data layer. With this practical book, data scientists and ML engineers will learn in detail how to develop, maintain, and operate modular ML systems.Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. Through examples, you'll look at how the feature store helps solve the hardest problem in ML-the data. When building systems, you'll move seamlessly from managing incremental datasets for training and fine-tuning to real-time data access and retrieval-augmented generation for online ML systems.With this book, you'll be able to:Make the leap from training ML models to building ML systemsDevelop an ML system as modular feature, training, and inference pipelinesDesign, develop, and operate batch ML systems, real-time ML systems, and fine-tuned LLM systems with retrieval-augmented generationLearn the problems a feature store for ML solves when building ML systemsUnderstand the principles of MLOps for developing and safely updating ML systemsJim Dowling is CEO of Hopsworks and an associate professor at KTH Royal Institute of Technology in Stockholm, Sweden.…

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paperback. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Paperback. Condition: New. Get up to speed on a new unified approach to building machine learning (ML) systems with batch data, real-time data, and large language models (LLMs) based on independent, modular ML pipelines and a shared data layer. With this practical book, data scientists and ML engineers will learn in detail how to develop, maintain, and operate modular ML systems.Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. Through examples, you'll look at how the feature store helps solve the hardest problem in ML-the data. When building systems, you'll move seamlessly from managing incremental datasets for training and fine-tuning to real-time data access and retrieval-augmented generation for online ML systems.With this book, you'll be able to:Make the leap from training ML models to building ML systemsDevelop an ML system as modular feature, training, and inference pipelinesDesign, develop, and operate batch ML systems, real-time ML systems, and fine-tuned LLM systems with retrieval-augmented generationLearn the problems a feature store for ML solves when building ML systemsUnderstand the principles of MLOps for developing and safely updating ML systemsJim Dowling is CEO of Hopsworks and an associate professor at KTH Royal Institute of Technology in Stockholm, Sweden.…

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Paperback. Condition: Brand New. 450 pages. 9.19x7.00x9.19 inches. In Stock.

Condition: New. 2025. paperback. . . . . .

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Paperback. Condition: New. Get up to speed on a new unified approach to building machine learning (ML) systems with batch data, real-time data, and large language models (LLMs) based on independent, modular ML pipelines and a shared data layer. With this practical book, data scientists and ML engineers will learn in detail how to develop, maintain, and operate modular ML systems.Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. Through examples, you'll look at how the feature store helps solve the hardest problem in ML-the data. When building systems, you'll move seamlessly from managing incremental datasets for training and fine-tuning to real-time data access and retrieval-augmented generation for online ML systems.With this book, you'll be able to:Make the leap from training ML models to building ML systemsDevelop an ML system as modular feature, training, and inference pipelinesDesign, develop, and operate batch ML systems, real-time ML systems, and fine-tuned LLM systems with retrieval-augmented generationLearn the problems a feature store for ML solves when building ML systemsUnderstand the principles of MLOps for developing and safely updating ML systemsJim Dowling is CEO of Hopsworks and an associate professor at KTH Royal Institute of Technology in Stockholm, Sweden.…

Condition: New. 2025. paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Condition: Neu. 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. …

Condition: New.

Paperback. Condition: new. Paperback. Get up to speed on a new unified approach to building machine learning (ML) systems with batch data, real-time data, and large language models (LLMs) based on independent, modular ML pipelines and a shared data layer. With this practical book, data scientists and ML engineers will learn in detail how to develop, maintain, and operate modular ML systems.Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. Through examples, you'll look at how the feature store helps solve the hardest problem in ML-the data. When building systems, you'll move seamlessly from managing incremental datasets for training and fine-tuning to real-time data access and retrieval-augmented generation for online ML systems.With this book, you'll be able to:Make the leap from training ML models to building ML systemsDevelop an ML system as modular feature, training, and inference pipelinesDesign, develop, and operate batch ML systems, real-time ML systems, and fine-tuned LLM systems with retrieval-augmented generationLearn the problems a feature store for ML solves when building ML systemsUnderstand the principles of MLOps for developing and safely updating ML systemsJim Dowling is CEO of Hopsworks and an associate professor at KTH Royal Institute of Technology in Stockholm, Sweden. Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condition: New. Get up to speed on a new unified approach to building machine learning (ML) systems with batch data, real-time data, and large language models (LLMs) based on independent, modular ML pipelines and a shared data layer. With this practical book, data scientists and ML engineers will learn in detail how to develop, maintain, and operate modular ML systems.Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. Through examples, you'll look at how the feature store helps solve the hardest problem in ML-the data. When building systems, you'll move seamlessly from managing incremental datasets for training and fine-tuning to real-time data access and retrieval-augmented generation for online ML systems.With this book, you'll be able to:Make the leap from training ML models to building ML systemsDevelop an ML system as modular feature, training, and inference pipelinesDesign, develop, and operate batch ML systems, real-time ML systems, and fine-tuned LLM systems with retrieval-augmented generationLearn the problems a feature store for ML solves when building ML systemsUnderstand the principles of MLOps for developing and safely updating ML systemsJim Dowling is CEO of Hopsworks and an associate professor at KTH Royal Institute of Technology in Stockholm, Sweden.…

Taschenbuch. Condition: Neu. Building Machine Learning Systems with a Feature Store | Batch, Real-Time, and LLM Systems | Jim Dowling | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | O'Reilly Media | EAN 9781098165239 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …

Paperback. Condition: new. Paperback. Get up to speed on a new unified approach to building machine learning (ML) systems with batch data, real-time data, and large language models (LLMs) based on independent, modular ML pipelines and a shared data layer. With this practical book, data scientists and ML engineers will learn in detail how to develop, maintain, and operate modular ML systems.Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. Through examples, you'll look at how the feature store helps solve the hardest problem in ML-the data. When building systems, you'll move seamlessly from managing incremental datasets for training and fine-tuning to real-time data access and retrieval-augmented generation for online ML systems.With this book, you'll be able to:Make the leap from training ML models to building ML systemsDevelop an ML system as modular feature, training, and inference pipelinesDesign, develop, and operate batch ML systems, real-time ML systems, and fine-tuned LLM systems with retrieval-augmented generationLearn the problems a feature store for ML solves when building ML systemsUnderstand the principles of MLOps for developing and safely updating ML systemsJim Dowling is CEO of Hopsworks and an associate professor at KTH Royal Institute of Technology in Stockholm, Sweden. Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condition: Brand New. 450 pages. 9.19x7.00x9.19 inches. In Stock. This item is printed on demand.

Paperback. Condition: new. Paperback. Get up to speed on a new unified approach to building machine learning (ML) systems with batch data, real-time data, and large language models (LLMs) based on independent, modular ML pipelines and a shared data layer. With this practical book, data scientists and ML engineers will learn in detail how to develop, maintain, and operate modular ML systems.Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. Through examples, you'll look at how the feature store helps solve the hardest problem in ML-the data. When building systems, you'll move seamlessly from managing incremental datasets for training and fine-tuning to real-time data access and retrieval-augmented generation for online ML systems.With this book, you'll be able to:Make the leap from training ML models to building ML systemsDevelop an ML system as modular feature, training, and inference pipelinesDesign, develop, and operate batch ML systems, real-time ML systems, and fine-tuned LLM systems with retrieval-augmented generationLearn the problems a feature store for ML solves when building ML systemsUnderstand the principles of MLOps for developing and safely updating ML systemsJim Dowling is CEO of Hopsworks and an associate professor at KTH Royal Institute of Technology in Stockholm, Sweden. Author Jim Dowling introduces fundamental MLOps principles and practices for developing and operating reliable ML systems and describes the key data platform that you'll use to build and operate your ML systems: the feature store. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…