Machine learning system design : with end-to-end examples / Valerii Babushkin, Arseny Kravchenko.

Includes index.

Part 1. Preparations. Essentials of machine learning system design -- Is there a problem? -- Preliminary research -- Design document -- Part 2. Early stage. Loss functions and metrics -- Gathering datasets -- Validation schemas -- Baseline solution -- Part 3. Intermediate steps. Error analysis -- Training pipelines -- Features and feature engineering -- Measuring and reporting results -- Part 4. Integration and growth. Integration -- Monitoring and reliability -- Serving and inference optimization -- Ownership and maintenance.

"Designing and delivering a machine learning system is an intricate multistep process that requires many skills and roles. Whether you're an engineer adding machine learning to an existing application or designing a ML system from the ground up, you need to navigate massive datasets and streams, lock down testing and deployment requirements, and master the unique complexities of putting ML models into production. That's where this book comes in. Machine Learning System Design shows you how to design and deploy a machine learning project from start to finish. You'll follow a step-by-step framework for designing, implementing, releasing, and maintaining ML systems. As you go, requirement checklists and real-world examples help you prepare to deliver and optimize your own ML systems. You'll especially love the campfire stories and personal tips, and ML system design interview tips"--

CSE