Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples Machine Learning Engineering with Python: Manage the lifecycle of machine learning models using MLOps with practical examples Paperback Kindle
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From the Publisher
Deep dive into the fundamentals
Practical problem-solving
Apply your acquired skills and knowledge to solve real-world problems with examples and hands-on exercises. You can use your practical knowledge to solve production issues and scale them to any size based on your needs.
You’ll learn what good ML engineering processes look like. You’ll discover how to choose, train, and even automate your models. You’ll gain an understanding of what aspects of software engineering you should bring to ML, and explore all the newest libraries and tools available.
The book focuses on teaching you the fundamentals and introducing you to all the key aspects needed for a successful ML engineering career. You'll explore best practices, tips, and tricks for software development.
Explore best practices for machine learning engineering
Automate training and deployment for your ML processes
Build wrapper libraries for encapsulating your data science and ML logic and solutions
Learn > Apply > Master
Learn like a professional
Cover the basics, learn what ML engineering is, how to do it well, and how to design your own process.
Start small, but go big
Put your knowledge to the test by training, deploying, and scaling your solutions.
Test yourself
Work through real-world end-to-end scenarios to really cement your new skills.
Get to grips with the biggest libraries and packages
This latest edition of Machine Learning Engineering with Python is packed with new information and techniques. Building on the solid foundation of the first edition, more technical depth has been introduced with the example chapters revamped completely.