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
Nº de artículo: 45099508

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

Nº de artículo: 45099508

NIO 2230

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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 Nicaragua
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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Preguntas y respuestas de los clientes

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

    Respuesta: 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.
  • Pregunta: What will I learn from this book?

    Respuesta: 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.
  • Pregunta: Does the book include practical examples?

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

Computer Science Editorial Review

**** "Machine Learning Engineering with Python" emerges as a pivotal resource for intermediate data scientists and ML engineers seeking a deeper understanding of machine learning implementation in real-world scenarios. Unlike many books that concentrate on theoretical models or isolated ML frameworks, this guide emphasizes practical applications and essential MLops tools that enhance the ability to train, deploy, serve, and iterate on models effectively. The author successfully addresses a significant gap in the understanding of implementation techniques by integrating multiple real-time and batch example scenarios. These practical illustrations not only elucidate critical areas such as versioning, model retraining due to data drift, and automation of hyperparameters, but also dive into deployment and scaling methodologies—particularly noteworthy in chapters on deployment patterns and scaling strategies. Readers have found value in the clarity of explanations, visual aids like diagrams, and organized breakdowns of complex concepts, making it easier to absorb information. Furthermore, the book's repository, offering example datasets and code in Python notebooks, has been a highlight for many, facilitating hands-on learning and practical application. However, some critiques have surfaced regarding the book's focus on AWS for deployment, potentially alienating users of Azure or Google Cloud. Additionally, the end-to-end examples presented may not fully encapsulate the detailed coding necessary for newcomers, suggesting an area for improvement for future editions. Overall, the book serves as an excellent guide into the practical aspects of machine learning engineering, making it a compelling read for professionals eager to enhance their skillset and implement ML solutions in their organizations effectively. **

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ventajas

  • Comprehensive coverage of MLops tools and real-world applications.
  • Good balance of theory and practical exercises, especially for intermediate users.
  • Clear and detailed explanations of deployment patterns and scaling strategies.
  • Helpful visual aids enhance understanding.
  • Useful repositories with datasets and code examples.

Contras

  • Limited focus on cloud platforms other than AWS might exclude some readers.

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características y beneficios

  • 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