Getting machine learning (ML) models into production continues to remain challenging using traditional software development methods. This book highlights the changing trends of software development over time and solves the problem of integrating ML with traditional software using MLOps.
In this new edition of Engineering MLOps, Emmanuel Raj demystifies MLOps to equip you with the skills needed to build your own MLOps pipelines using -of-the-art tools (MLFlow, DVC, KubeFlow, Locust.io, Docker, Kubernetes, Apache Spark, to name a few) and platforms. You will start by learning the essentials of ML engineering and build ML pipelines to train and deploy models. The book then covers how to implement an MLOps solution for a real-life business problem using Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), as well as cloud agnostic tools. You'll also understand how to build continuous integration/deployment (CI/CD) and continuous delivery pipelines to build, test, deploy, and monitor your models.
By the end of the book, you will become proficient at building, deploying, and monitoring any ML model with the MLOps process using any tool or platform.

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Spis treści

Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale on Azure - Second Edition eBook -- spis treści

• 1. Introduction to MLOps
• 2. MLOps for Business
• 3. Basics of ML Engineering
• 4. Characterizing your Machine Learning Problem for MLOps
• 5. Machine Learning Pipelines
• 6. Model Evaluation and Packaging
• 7. Deploying your models as a batch or live endpoint
• 8. Deploying your models as a streaming service
• 9. Building robust CI and CD pipelines
• 10. API and microservice Management
• 11. Essentials of testing release
• 12. Essentials of production release
• 13. Key principles for monitoring your ML system
• 14. Model serving and metrics selection
• 15. Continuous delivery and continuous monitoring
• 16. Orchestrating ML pipelines in Azure Synapse
• 17. Governance of ML Solutions
• 18. Building Machine Learning Models
• 19. Deploying Machine Learning Models
• 21. MLOps Inspiration and Use Cases
• 22. Building a Technical Portfolio for MLOps
• 23. Monitoring and Governance