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Machine Learning Engineering with Python – Second Edition is a comprehensive guide to building, deploying, and maintaining machine learning applications. The book is written for practitioners who want to learn how to apply machine learning in real-world production environments.
Key features of the book:
Hands-on approach: The book includes a number of hands-on projects that will give readers the opportunity to practice the skills they learn.
Focus on production-ready machine learning: The book covers the entire machine learning lifecycle, from data preparation and model training to deployment and monitoring.
Use of Python and TensorFlow: The book uses Python and TensorFlow to build and deploy machine learning models.
Coverage of real-world applications: The book includes case studies of real-world machine learning applications.
Updated for the latest versions of Python and TensorFlow: The second edition of the book is updated for the latest versions of Python and TensorFlow.
Overall, “Machine Learning Engineering with Python – Second Edition” is an essential resource for anyone who wants to learn how to build and deploy machine learning applications in production.
Here are some of the pros and cons of the book:
Pros:
Comprehensive coverage of machine learning engineering
Hands-on approach
Focus on production-ready machine learning
Use of Python and TensorFlow
Coverage of real-world applications
Updated for the latest versions of Python and TensorFlow
Cons:
May be too technical for some readers
Could benefit from more in-depth coverage of some topics
