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Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines
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Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines

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240,000₫

Sách Machine Learning Production Systems Engineering Machine Learning Models and Pipelines

Sách keo gáy, bìa mềm
 
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
 
 
Categories:Computers - Artificial Intelligence (AI)
 
Year:2024
 
Edition:1
 
Language:english
 
Pages:475
 

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