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Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
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Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments.
Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples
物品 #: 45099508

Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples

物品 #: 45099508

HKD 470

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Learn how to manage the production life cycle of machine learning models using MLOps techniques. Get practical examples and master Python! Shop at Ubuy 香港
Item Weight2 lbs (910 grams)

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Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples

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客戶問題＆回答

  • 題: Who is the target audience for this book?

    回答: This book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. Intermediate-level knowledge of Python is necessary.
  • 題: What will I learn from this book?

    回答: You will learn how to build scalable and robust solutions that can serve your machine learning models in production environments, including hyperparameter optimization, model management, building ML libraries and packages, exploring ML engineering patterns, toolsets for training and deployment, and using cloud-based tools.
  • 題: Does the book include practical examples?

    回答: Yes, the book includes practical illustrations and examples to help you solve typical business problems.

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特色和優勢

  • Learn hyperparameter optimization and model management tools
  • Build your own ML libraries and packages in Python
  • Explore ML engineering patterns
  • Create toolsets for training and deployment
  • Work with deployment architectures and cloud-based tools
  • Solve typical business problems with examples