Machine Learning System Design With end-to-end examples (Valerii Babushkin, Arseny Kravchenko) (Z-Library)

Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems. From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside, you’ll find a reliable framework for building, maintaining, and improving machine learning systems at any scale or complexity. In Machine Learning System Design: With end-to-end examples you will learn: • The big picture of machine learning system design • Analyzing a problem space to identify the optimal ML solution • Ace ML system design interviews • Selecting appropriate metrics and evaluation criteria • Prioritizing tasks at different stages of ML system design • Solving dataset-related problems with data gathering, error analysis, and feature engineering • Recognizing common pitfalls in ML system development • Designing ML systems to be lean, maintainable, and extensible over time Authors Valeri Babushkin and Arseny Kravchenko have filled this unique handbook with campfire stories and personal tips from their own extensive careers. You’ll learn directly from their experience as you consider every facet of a machine learning system, from requirements gathering and data sourcing to deployment and management of the finished system. What's inside • Metrics and evaluation criteria • Solve common dataset problems • Common pitfalls in ML system development • ML system design interview tips About the reader For readers who know the basics of software engineering and machine learning. Examples in Python.