Practical Machine Learning System Design: Engineering for Scale and Performance
Practical Machine Learning System Design: Engineering for Scale and Performance
A Comprehensive Guide to Building Scalable, Robust, and Production-Ready AI Applications
About the Book
Building Scalable Machine Learning Systems: A Comprehensive Guide is a practical and in-depth resource designed to help data scientists, ML engineers, and software developers design, deploy, and maintain robust machine learning systems in real-world production environments. This book goes beyond model development, focusing on the end-to-end architecture of ML systems, including data pipelines, scalability, deployment strategies, and monitoring.
Readers will learn how to tackle common challenges such as data drift, model decay, and ethical considerations while optimizing for performance, fairness, and scalability. Through real-world case studies, best practices, and actionable insights, this book provides the tools and frameworks needed to build ML systems that are not only accurate but also reliable, scalable, and maintainable.
Whether you're preparing for ML system design interviews or looking to improve your production ML workflows, this book offers a structured approach to mastering the art of building machine learning systems that deliver real-world value.
About the Author
Table of Contents
- Practical Machine Learning Systems Design: A Comprehensive Guide
- Table of Contents
- Chapter 1: Introduction
- Chapter 2: Understanding the Problem
- Chapter 3: Data Foundations
- Chapter 4: Model Selection and Development
- Chapter 5: Building for Scalability
- Chapter 6: Testing and Deployment
- Chapter 7: Handling Real-World Challenges
- Chapter 8: ML System Design Interviews & Best Practices
- Conclusion
- Practical Machine Learning Systems Design: A Comprehensive Guide
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