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.

Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.

Machine learning system design is complex. The successful ML engineer needs to navigate a multistep process that demands skills from many different fields and roles. This one-of-kind-guide starts by showing you the big picture and then guides you step by step through a framework for creating successful systems.

You'll learn to excel at delivering for global objectives, diving locally into tools, and combining your knowledge into an integrated vision.

In Machine Learning System Design: With end-to-end examples you'll find a step-by-step framework for creating, implementing, releasing, and maintaining your ML system. Every part of the life cycle is covered, from information gathering to keeping your system well-serviced. Each stage includes its own handy checklist of requirements and is fully illustrated with real-world examples, including interesting anecdotes from the author's own careers.

You'll follow two example companies each building a new ML system, exploring how their needs are expressed in design documents and learning best practices by writing your own.

Along the way, you'll learn how to ace ML system design interviews, even at highly competitive FAANG-like companies, and improve existing ML systems by identifying bottlenecks and optimizing system performance.

For readers who know the basics of both software engineering and machine learning. Examples in Python.