Building Machine Learning Pipelines
Automating Model Life Cycles with TensorFlow
(Sprache: Englisch)
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...
Erscheint am 30.09.2024
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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, Di Zhu, and Catherine Nelson 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
Autoren-Porträt von Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu, Catherine Nelson
Robert Crowe is a data scientist and TensorFlow enthusiast. Robert has a passion for helping developers quickly learn what they need to be productive. Robert is the Senior Product Manager for TensorFlow Open-Source and MLOps at Google and helps ML teams meet the challenges of creating products and services with ML. Previously, Robert led software engineering teams for both large and small companies, always focusing on clean, elegant solutions to well-defined needs.
Bibliographische Angaben
- Autoren: Robert Crowe , Hannes Hapke , Emily Caveness , Di Zhu , Catherine Nelson
- 2024, 2nd Edition, 260 Seiten, Kartoniert (TB), Englisch
- Verlag: O'Reilly Media
- ISBN-10: 1098156013
- ISBN-13: 9781098156015
- Erscheinungsdatum: 30.09.2024
Sprache:
Englisch
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