Comprehensive guide to designing and building production-ready machine learning systems. This book provides end-to-end examples covering data pipelines, model training, deployment, monitoring, and scaling ML systems. Essential for ML engineers, data scientists, and software engineers building ML-powered applications.

Machine Learning System Design is an essential guide for anyone building production ML systems. The book goes beyond just training models to cover the full lifecycle of ML systems—from data collection and preprocessing to model deployment, monitoring, and scaling. The end-to-end examples are particularly valuable, showing how all the pieces fit together in real-world scenarios. Authors Valerii Babushkin and Arseny Kravchenko provide practical guidance on designing ML systems that are scalable, maintainable, and reliable. The book covers critical topics like data pipelines, feature stores, model versioning, A/B testing, monitoring and observability, and handling model drift. Whether you're an ML engineer, data scientist, or software engineer working on ML-powered applications, this book provides the system design knowledge you need to build production-ready ML systems. The focus on end-to-end examples makes it particularly valuable for understanding how to architect complete ML solutions rather than just individual components.

Comprehensive guide to designing production-ready machine learning systems with end-to-end examples covering data pipelines, training, and deployment.

Machine Learning System Design: End-to-End Examples

Machine Learning System Design: With end-to-end examples

Hand-picked ml infrastructure tools and resources we actually use in production. Each item tested by developers, for developers.

ML Infrastructure Products

Machine Learning System Design: With end-to-end examples