Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale

English | 2021 | ISBN: 978-1800562882 | 282 Pages | PDF, EPUB, MOBI | 82 MB

Get up and running with machine learning life cycle management and implement MLOps in your organization

Key Features
Become well-versed with MLOps techniques to monitor the quality of machine learning models in production
Explore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed models
Perform CI/CD to automate new implementations in ML pipelines

MLOps is a systematic approach to building, deploying, and monitoring machine learning solutions. It is an engineering discipline that can be applied to various industries and use cases. This book presents comprehensive insights into MLOps coupled with real-world examples to help you to write programs, train robust and scalable machine learning (ML) models, and build ML pipelines to train and deploy models securely in production.

The book begins by showing you how to monitor ML and system performance in production. 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 understand how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitoring 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 machine learning book, you’ll have a 360-degree view of MLOps and be ready to implement MLOps in your organization.

What you will learn

• Formulate data governance strategies and pipelines for machine learning training and deployment
• Get to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelines
• Design a robust and scalable microservice and API for test and production environments
• Curate your custom CD processes for related use cases and organizations
• Monitor ML models, including monitoring data drift, model drift, and application performance
• Build and maintain automated ML systems