Description

Master the art of designing robust, scalable machine learning systems with Chip Huyen’s comprehensive guide to production-ready ML applications. This essential resource takes you beyond model building to tackle real-world challenges of deploying, monitoring, and maintaining ML systems at scale.

Learn the iterative framework for making critical design decisions including data processing strategies, feature selection, model retraining schedules, and production monitoring. Backed by actual case studies and extensive references, this book addresses complex scenarios from engineering data pipelines to architecting ML platforms that serve multiple use cases.

• Holistic ML System Design – Comprehensive approach to building reliable, scalable, and maintainable production ML systems

• Iterative Framework Methodology – Proven process for making design decisions from data processing to model deployment and monitoring

• Real-World Case Studies – Practical examples backed by extensive research and industry best practices from Claypot AI co-founder

• Production-Ready Focus – Emphasis on deployment, monitoring, and maintenance rather than just model development

• Cross-Functional Guidance – Addresses needs of ML engineers, data scientists, software architects, and technical stakeholders

• Automation & MLOps – Covers continuous development, evaluation, deployment, and model updating processes

• Responsible ML Practices – Guidelines for developing ethical, fair, and accountable machine learning systems

• Enterprise Architecture – Strategies for building ML platforms that scale across multiple use cases and business requirements