Practical MLOps by Noah Gift

About the Book

Practical MLOps bridges the gap between machine learning experimentation and production deployment. Written by industry experts Noah Gift and Alfredo Deza, this comprehensive guide walks you through the entire MLOps lifecycle—from model development and testing to continuous integration, monitoring, and scaling. You’ll learn how to automate ML workflows, implement best practices for model governance, and build systems that deliver reliable predictions at scale. This isn’t just theory; it’s a practical roadmap filled with real-world examples, code samples, and architectural patterns you can apply immediately.

About the Author

Noah Gift is an executive in residence at the Graduate Data Science program at Northwestern University and teaches MLOps at Duke University. With decades of experience in cloud computing, AI, and DevOps, he’s helped countless organizations implement successful machine learning operations. Alfredo Deza is a software engineer with extensive experience in automation, testing, and distributed systems. Together, they bring both academic insight and battle-tested industry knowledge to help you master the complexities of putting ML into production.

Who Is This Book For?

This book is designed for data scientists who want to move beyond model training, software engineers building ML infrastructure, DevOps professionals integrating ML into CI/CD pipelines, and ML engineers responsible for deploying and maintaining models. If you’re tired of models that work perfectly in development but fail in production, or if you’re struggling with version control, monitoring, and scaling challenges, this guide provides the frameworks and techniques you need to build reliable, maintainable ML systems that deliver consistent business value.

Frequently Asked Questions (FAQs)

Q: What is MLOps and why do I need to learn it?
MLOps (Machine Learning Operations) is the practice of deploying, monitoring, and maintaining ML models in production. You need it because building models is only 20% of the work—the real challenge is making them reliable, scalable, and maintainable in business environments where decisions depend on them.

Q: Do I need prior experience with DevOps or cloud platforms?
Basic familiarity with programming and machine learning concepts is helpful, but the book explains MLOps fundamentals from the ground up. You’ll learn cloud deployment, containerization with Docker, and CI/CD practices specifically tailored for ML workflows throughout the chapters.

Q: Which tools and platforms does Practical MLOps cover?
The book covers industry-standard MLOps tools including Docker, Kubernetes, GitHub Actions, AWS SageMaker, Azure ML, and popular Python frameworks. You’ll learn platform-agnostic principles that apply across different cloud providers and toolchains.

Q: How is MLOps different from traditional DevOps?
MLOps extends DevOps practices to handle unique ML challenges like data versioning, model reproducibility, experiment tracking, feature store management, and model drift detection. Traditional DevOps focuses on code deployment; MLOps manages the entire lifecycle of data, models, and predictions.