"Azure ML Pipelines in Practice"
Azure ML Pipelines in Practice is a comprehensive guide for machine learning engineers, data scientists, and DevOps professionals seeking to master the design, deployment, and management of end-to-end ML pipelines on the Azure platform. Beginning with fundamental concepts and architecture, the book navigates through core pipeline frameworks, secure environment setup, and orchestration strategies, providing readers with the practical knowledge needed to harness the full power of Azure Machine Learning services. Each chapter is meticulously structured to build both theoretical understanding and operational competence, addressing critical topics such as security, identity management, and environment configuration.
Moving beyond the basics, the book delves into the intricacies of data engineering, scalable component design, and advanced workflow orchestration. Readers will learn essential techniques for data integration, versioning, and transformation, together with robust approaches to validation and privacy compliance. The treatment of modular and reusable component development is complemented by in-depth coverage of error handling, conditioning, parallelism, and efficient resource management—empowering practitioners to create maintainable, testable, and production-grade pipelines.
The later chapters focus on real-world applications, including distributed training, hyperparameter tuning, automated model evaluation, and deployment automation. The book addresses CI/CD integration, infrastructure-as-code strategies, and operational monitoring for ongoing pipeline health, while also tackling the nuances of scaling, governance, cost management, and global deployment across enterprise environments. Advanced patterns and emerging directions—such as hybrid and multi-cloud orchestration, event-driven flows, edge/IoT integration, and extensibility with open-source tools—round out the volume, making Azure ML Pipelines in Practice an indispensable resource for building resilient and future-ready ML workflows in the cloud.
