В. Бабушкин, А.Кравченко | Машинное обучение. Проектирование систем от идеи до реализации [2 книги] (2025, 2026) [PDF, EPUB, MOBI, AZW3] [RU, EN]

Краткое содержание

Часть 1. Подготовка к работе
Глава 1. Основы проектирования систем машинного обучения
Глава 2. В чем проблема?
Глава 3. Предварительное изучение
Глава 4. Дизайн-документ

Часть 2. Первые шаги
Глава 5. Функции потерь и метрики
Глава 6. Подготовка датасета
Глава 7. Схемы валидации
Глава 8. Базовая модель

Часть 3. Промежуточные этапы
Глава 9. Анализ ошибок
Глава 10. Пайплайны обучения
Глава 11. Признаки и генерация признаков
Глава 12. Измерение и представление результатов

Часть 4. Интеграция и масштабирование
Глава 13. Интеграция
Глава 14. Мониторинг и надежность
Глава 15. Сервинг и оптимизация инференса
Глава 16. Владение и сопровождение

Об авторах
Валерий Бабушкин — опытный руководитель в области анализа данных с обширным опытом работы в технологической индустрии. В настоящее время он занимает должность вице-президента по анализу данных в Blockchain.com, где отвечает за руководство инициативами компании, основанными на данных. До прихода в Blockchain.com Валерий занимал ключевые должности в ведущих технологических компаниях, таких как Facebook, Alibaba и X5 Retail Group.
Арсений Кравченко — опытный инженер по машинному обучению с подтвержденным опытом создания и оптимизации надежных систем машинного обучения для стартапов, включая обработку видео в реальном времени, оптимизацию производства и анализ финансовых транзакций.

В оригинале
Machine Learning System Design: With end-to-end examples by Valerii Babushkin, Arseny Kravchenko

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 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 with 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.
Authors 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 inside
Metrics and evaluation criteria.
Solve common dataset problems.
Common pitfalls in ML system development.
ML system design interview tips.

About 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 in the tech industry. He currently serves as the VP of Data Science at Blockchain.com, where he is responsible for leading the company's data-driven initiatives. Prior to joining Blockchain.com, Valerii held key roles at leading tech companies, such as Facebook, Alibaba, and X5 Retail Group.
Arseny Kravchenko is a seasoned ML engineer with a proven track record of building and optimizing reliable ML systems for startups, including real-time video processing, manufacturing optimization, and financial transactions analysis.

Table of Contents

Part 1
Essentials of machine learning system design
Is there a problem?
Preliminary research
Design document

Part 2
Loss functions and metrics
Gathering datasets
Validation schemas
Baseline solution

Part 3
Error analysis
Training pipelines
Features and feature engineering
Measuring and reporting results

Part 4
Integration
Monitoring and reliability
Serving and inference optimization
Ownership and maintenance