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Machine Learning Engineering in Action

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Machine Learning Engineering in Action is a roadmap to delivering successful machine learning projects. It teaches you to adopt an efficient, sustainable, and goal-driven approach that author Ben Wilson has developed over a decade of data science experience. Every method in this book has been used to solve a breakdown in a real-world project, and is illustrated with production-ready source code and easily reproducible examples.

Learn more about the book here: http://mng.bz/KMqZ

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Machine Learning Engineering in Action

  1. 1. Dependable machine learning systems with Machine Learning Engineering in Action. Take 40% off by entering slwilson4 into the discount code box at checkout at manning.com.
  2. 2. Following established processes and methodology maximizes the likelihood that your machine learning projects will survive and succeed for the long haul. By adopting standard, reproducible practices, your projects will be maintainable over time and easy for new team members to understand and adapt.
  3. 3. Machine Learning Engineering in Action is a roadmap to delivering successful machine learning projects. It teaches you to adopt an efficient, sustainable, and goal- driven approach to your projects. You’ll learn many field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production.
  4. 4. In this book, you’ll learn how to plan and scope your project, manage cross-team logistics that avoid fatal communication failures, and design your code’s architecture for improved resilience. You’ll even discover when not to use machine learning—and the alternative approaches that might be cheaper and more effective. When you’re done working through this toolbox guide, you’ll be able to reliably deliver cost-effective solutions for organizations big and small alike.
  5. 5. Here’s some of what you’ll learn in the book • Evaluating data science problems to find the most effective solution • Scoping a machine learning project for usage expectations and budget • Process techniques that minimize wasted effort and speed up production • Assessing a project using standardized prototyping work and statistical validation • Choosing the right technologies and tools for your project • Making your codebase more understandable, maintainable, and testable • Leveraging MLFlow for automating logging and establishing provenance
  6. 6. About the author: Ben Wilson has worked as a professional data scientist for more than ten years. He currently works as a practice lead at Databricks, focusing on machine learning production architecture for various companies. Ben is the creator and lead developer of the Databricks Labs AutoML project, a Scala-and Python-based toolkit that simplifies machine learning feature engineering, model tuning, and pipeline-enabled modeling.
  7. 7. Take 40% off Machine Learning Engineering in Action by entering slwilson4 into the discount code box at checkout at manning.com. If you want to learn more about the book, you can check it out on our browser-based liveBook platform here.

Machine Learning Engineering in Action is a roadmap to delivering successful machine learning projects. It teaches you to adopt an efficient, sustainable, and goal-driven approach that author Ben Wilson has developed over a decade of data science experience. Every method in this book has been used to solve a breakdown in a real-world project, and is illustrated with production-ready source code and easily reproducible examples. Learn more about the book here: http://mng.bz/KMqZ

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