Machine Learning System Design: With End-To-End Examples

By Arseny Kravchenko and Valerii Babushkin

Book Overview

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 designAnalyzing a problem space to identify the optimal ML solutionAce ML system design interviewsSelecting appropriate metrics and evaluation criteriaPrioritizing tasks at different stages of ML system designSolving dataset-related problems with data gathering, error analysis, and feature engineeringRecognizing common pitfalls in ML system developmentDesigning ML systems to be lean, maintainable, and extensible over timeAuthors 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.

About the technology

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.

About the book

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.

What's insideMetrics and evaluation criteriaSolve common dataset problemsCommon pitfalls in ML system developmentML system design interview tipsAbout the reader

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

About the author

Valerii Babushkin is an accomplished data science leader with extensive experience. He currently serves as a Senior Principal at BP. Arseny Kravchenko is a seasoned ML engineer currently working as a Senior Staff Machine Learning Engineer at Instrumental.

Table of Contents

Part 1
1 Essentials of machine learning system design
2 Is there a problem?
3 Preliminary research
4 Design document
Part 2
5 Loss functions and metrics
6 Gathering datasets
7 Validation schemas
8 Baseline solution
Part 3
9 Error analysis
10 Training pipelines
11 Features and feature engineering
12 Measuring and reporting results
Part 4
13 Integration
14 Monitoring and reliability
15 Serving and inference optimization
16 Ownership and maintenance

• Format: Paperback
• Language: English
• ISBN: 1633438759
• ISBN13: 9781633438750
• Release Date: February 2025
• Publisher: Manning Publications
• Length: 376 Pages
• Weight: 1.50 lbs.
• Dimensions: 0.8" x 7.3" x 9.3"