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Machine Learning Production Systems Engineering Machine Learning Models and Pipelines (True PDF)

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Machine Learning Production Systems Engineering Machine Learning Models and Pipelines (True PDF)



English | November 5, 2024 | ISBN: 1098156013 | True PDF | 472 pages | 17.8 MB

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

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