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Engineering MLOps
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Engineering MLOps
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Overview of this book
Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.
The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you’ll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You’ll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you’ll apply the knowledge you’ve gained to build real-world projects.
By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization.
Table of Contents (18 chapters)
Preface
Section 1: Framework for Building Machine Learning Models
Free Chapter
Chapter 1: Fundamentals of an MLOps Workflow
Chapter 2: Characterizing Your Machine Learning Problem
Chapter 3: Code Meets Data
Chapter 4: Machine Learning Pipelines
Chapter 5: Model Evaluation and Packaging
Section 2: Deploying Machine Learning Models at Scale
Chapter 6: Key Principles for Deploying Your ML System
Chapter 7: Building Robust CI/CD Pipelines
Chapter 8: APIs and Microservice Management
Chapter 9: Testing and Securing Your ML Solution
Chapter 10: Essentials of Production Release
Section 3: Monitoring Machine Learning Models in Production
Chapter 11: Key Principles for Monitoring Your ML System
Chapter 12: Model Serving and Monitoring
Chapter 13: Governing the ML System for Continual Learning
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