ML Ops on Azure: From Models to Production delivers a comprehensive, hands-on roadmap for mastering machine learning operations (MLOps) using Microsoft Azure. Designed for ML engineers, data scientists, and cloud architects, this guide takes readers beyond experimentation to fully operationalizing machine learning workflows.

With the rapid growth of AI in enterprise environments, deploying models at scale is no longer optional—it's essential. This book provides an in-depth look at the key components of MLOps within the Azure ecosystem, including Azure Machine Learning, DevOps integration, automated pipelines, version control, model monitoring, and governance.

Starting with foundational concepts, readers will learn how to structure reproducible ML workflows, collaborate efficiently across teams, and implement continuous integration and continuous delivery (CI/CD) pipelines for model training and deployment. Real-world use cases, diagrams, and code examples provide clarity and actionable insights throughout the book.

Key features include:

Step-by-step implementation of MLOps using Azure ML

Building and automating ML pipelines

Versioning data, code, and models

Integrating GitHub Actions and Azure DevOps

Monitoring model performance and managing drift

Ensuring compliance and governance at scale

Whether you're transitioning from Jupyter notebooks to enterprise-grade systems or seeking to streamline existing ML operations, this book equips you with the tools and knowledge to build scalable, secure, and maintainable AI solutions on Azure.

Take your models from concept to production with confidence—and unlock the full potential of MLOps in the cloud.