Machine Learning Production Systems
Idioma: inglés
Editorial: O'Reilly Media, 2024
- Tapa blanda
- Nuevo
Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
Vendedor de AbeBooks desde 11 de junio de 1999
Condición: Nuevo
EUR 51,12
Cantidad disponible: 5 disponibles
Añadir al carritoDescripción del artículo del vendedor
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N° de ref. del artículo WO-9781098156015
- Título
- Machine Learning Production Systems
- Autor
- Robert Crowe
- Editorial
- O'Reilly Media
- Año de publicación
- 2024
- Estado
- New
- Encuadernación
- PAP
- Idioma
- inglés
- ISBN 10
- 1098156013
- ISBN 13
- 9781098156015
- Peso del artículo
- 893 gramos
Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting--especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field.
Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle.
This book provides four in-depth sections that cover all aspects of machine learning engineering:
- Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage
- Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search
- Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging
- Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Hannes Hapke is a Senior Machine Learning Engineer at Digits, and has co-authored multiple machine learning publications, including the book "Building Machine Learning Pipelines" by O'Reilly Media. He has also presented state-of-the-art ML work at conferences like ODSC or O'Reilly's TensorFlow World and is an active contributor to TensorFlow's TFX Addons project. Hannes is passionate about machine learning engineering and production machine learning use cases using the latest machine learning developments.
Emily Caveness is a software engineer at Google. She currently works on ML data analysis and validation.
Di Zhu is an engineer at Google. She has worked on a variety of projects, including MLOps infrastructure, applied machine learning solutions for different verticals including vision, ranking, dynamic pricing, etc. She is passionate about using engineering to solve real-world problems, designing and delivering MLOps solutions for several critical Google products and external partners. In addition to professional pursuits, Di is also a tennis player, Latin dancing competitor, and piano player.
“Acerca de” puede pertenecer a otra edición de este título.
PBShop.store UK
Fairford, GLOS, Reino Unido
Vendedor de AbeBooks desde 11 de junio de 1999
Tarifas de envío de Reino Unido a Estados Unidos de America
| Artículo | De 10 a 20 días hábiles | De 10 a 20 días hábiles |
|---|---|---|
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