Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines 1st Edition free ebook download.

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Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines 1st Edition free ebook download

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Catalogue

Author(s):	Hannes Hapke, Robert Crowe, Emily Caveness, Di Zhu

Date:	2024	Format:	pdf	Language:	English	ISBN/ASIN:	1098156013
Pages:	472	OCR:		Quality:		ISBN13:
Uploader:	eternal	Upload Date:	10/29/2024 5:10:09 AM

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1098156013 pdf
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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	Title	Link	Password	Size (MB)	Thank to	Inform Admin
3274813	Book	https://nitroflare.com/view/7EEEC115A90D...		9.00	eternal

Tags: Engineering Learning Learning Machine Machine Models Pipelines Production Systems

Title	Link	Password	Size	Reason

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