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. 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 will be able to reliably deliver cost-effective solutions for organizations big and small alike.
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