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
товар №: 45099508

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

товар №: 45099508

UZS 995472

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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 Uzbekistan
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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Вопросы и ответы клиентов

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

    отвечать: 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.
  • вопрос: What will I learn from this book?

    отвечать: 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.
  • вопрос: Does the book include practical examples?

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

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Особенности и преимущества

  • 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