Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

Designing Machine Learning Systems by Chip Huyen

Build Reliable, Scalable & Production-Ready ML Systems

Machine learning systems are more than just models. They are complex ecosystems involving data, algorithms, infrastructure, deployment, monitoring, people, and business requirements. Because every ML application depends on its data and use case, designing a system that continues to perform in the real world requires a holistic approach.

Designing Machine Learning Systems by Chip Huyen, co-founder of Claypot AI, provides a practical framework for building reliable, scalable, maintainable, and adaptable machine learning systems.

Rather than focusing on individual components in isolation, the book examines how key engineering decisions work together to help an ML system achieve its overall objectives.

Learn How to Design Production-Ready ML Systems

Through practical guidance, real-world case studies, and extensive references, you'll explore important decisions involved in building and operating modern machine learning systems, including:

• Engineering data and selecting metrics that align with business objectives

• Choosing and creating effective training data and features

• Designing automated processes for developing, evaluating, deploying, and updating ML models

• Determining when and how often models should be retrained

• Building monitoring systems that detect and address production ML issues

• Designing an ML platform that can support multiple use cases

• Building machine learning systems that can adapt to changing data and business requirements

• Developing responsible machine learning systems

• Understanding the trade-offs behind important ML system design decisions

A Holistic Approach to Machine Learning Engineering

Building a successful ML system isn't simply about selecting the most sophisticated algorithm.

It's about understanding how data pipelines, models, infrastructure, evaluation, deployment, monitoring, and business goals fit together.

Chip Huyen presents an iterative approach supported by real-world examples and case studies, helping readers think beyond model development and understand the complete machine learning lifecycle.

Who Is This Book For?

Designing Machine Learning Systems is an essential resource for:

• Machine learning engineers

• Data scientists

• Software engineers

• ML platform engineers

• AI engineers

• Technical leaders and architects

• Professionals building and maintaining production ML systems

Whether you're designing your first production machine learning pipeline or improving an existing ML platform, this book provides practical insights for making better system-level decisions.

Move beyond building models. Learn how to design machine learning systems that are reliable, scalable, maintainable, and ready for the demands of the real world.

Key Topics

Machine Learning Systems · ML Engineering · MLOps · Data Engineering · Model Deployment · Model Monitoring · Feature Engineering · Training Data · ML Platforms · Responsible AI · Production Machine Learning · Machine Learning Architecture

Book Details

Author: Chip Huyen
Title: Designing Machine Learning Systems
Focus: Machine Learning Engineering, MLOps, Data Science & Production AI