Machine Learning System Design

Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems.

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 through 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

Machine Learning System Design: With end-to-end examples is a practical guide for planning and designing successful ML applications. It lays out a clear, repeatable framework for building, maintaining, and improving systems at any scale. Authors Arseny Kravchenko and Valeri Babushkin 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.