The book "Machine Learning System Design" by Valerii Babushkin and Arseniy Kravchenko is a practical guide to designing full-scale ML systems, going beyond classical machine learning. The author explains in detail how to build not just models, but the entire infrastructure around them: data collection, pipeline organization, deployment, monitoring, and production support.
This publication is intended for engineers and architects who prioritize not only model accuracy but also scalability, stability, fault tolerance, and seamless integration into business processes. You will learn how to work at the intersection of MLOps, Data Engineering, and Software Architecture, based on real-world scenarios and tasks. The book is ideal for those aiming to build a mature ML infrastructure — from experimentation to production.
Download the book "Machine Learning System Design" in PDF today. This is not just material about models — it is an engineering foundation for building mature AI products.
What are the key advantages of "Machine Learning System Design"?
One of the book’s main strengths is its comprehensive approach to ML projects: from initial prototypes to scaling and monitoring production systems. The author demonstrates that a successful system is not just a good model, but a combination of a resilient pipeline, metrics, logging, CI/CD processes, and a managed release cycle.
The book is particularly valuable for those who have encountered challenges in deploying models into real-world infrastructure: instability, lack of reproducibility, manual processes, and maintenance difficulties. Kravchenko provides proven architectural solutions that turn ML systems into an integral part of the engineering platform.
Special attention is given to the ML system lifecycle, including data versioning, experiment reproducibility, rollback mechanisms, and model observability. This makes the book a valuable resource not only for Data Scientists but also for ML Engineers and System Architects.
What will you learn from this book?
The book will teach you how to build a sustainable and scalable ML infrastructure that ensures the stable operation of models at every stage of their lifecycle.
- How to structure an end-to-end ML system
- How to design data preparation and training pipelines
- How to implement experiment reproducibility and versioning
- How to deploy models in production using CI/CD practices
- How to monitor metrics, detect quality degradation, and track model behavior
- How to manage dependencies, artifacts, and ensure system security
More About the Author of the Book
FAQ for "Machine Learning System Design"
1. Is the book suitable for those just starting out in ML?
The book is aimed at readers already familiar with basic machine learning concepts who want to advance to system-level design. If you know how to train models but struggle to integrate them into infrastructure, this guide will help bridge that gap.
2. Does the book cover DevOps and MLOps practices?
Yes. Several chapters are dedicated to automation through CI/CD, model deployment, containerization (Docker), experiment tracking systems (MLflow, Weights & Biases), and infrastructure management strategies.
3. Are real-world case studies and architectures provided?
Yes. The author presents practical ML system diagrams, common design patterns, and anti-patterns to avoid. Both monolithic and microservice architectures are discussed.
4. Does the book address working with large datasets?
Absolutely. It describes how to design pipelines using distributed systems such as Spark and Airflow, and how to work with features and streaming data sources in real time.
5. Are scalability and fault tolerance covered?
Definitely. The book offers strategies for building reliable systems: redundancy, monitoring, alerting, model degradation management, and disaster recovery. These are critical aspects for production-grade solutions.
Information
| Author: | Valerii Babushkin, Arseny Kravchenko | Language: | English |
| Publisher: | Manning | ISBN-13: | 978-1633438750 |
| Publication Date: | February 25, 2025 | ISBN-10: | 1633438759 |
| Print Length: | 376 pages | Category: | Machine Learning and Artificial Intelligence Books |
Free download "Machine Learning System Design" by Valerii Babushkin, Arseny Kravchenko in PDF
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