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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
Kalyan Chakravarthy Kodela is a seasoned professional and technology manager with over 15 years of experience in designing and implementing scalable machine learning and data engineering solutions. With deep expertise in Microservices, Micro Frontends, Cloud Native Applications, Data Governance, Data Engineering, Snowflake, Databricks, Machine Learning, Artificial Intelligence, Azure Data Services, and big data ecosystems, he has led teams in building high-performance ML systems across finance, healthcare, and enterprise analytics.
Throughout his career, Kalyan has focused on bridging the gap between cutting-edge machine learning research and real-world applications. He is passionate about optimizing ML pipelines, improving system scalability, and ensuring that AI-driven solutions deliver tangible business value.
This book reflects his practical approach to ML system design, offering a structured guide for engineers, data scientists, and technology leaders looking to build robust, maintainable, and efficient machine learning systems. When not working on ML architecture, Kalyan enjoys mentoring, contributing to open-source projects, and staying at the forefront of emerging AI technologies.