Engineering MLOps

Engineering MLOps

eBook Details:

  • Paperback: 370 pages
  • Publisher: WOW! eBook (April 19, 2021)
  • Language:¬†English
  • ISBN-10: 1800562888
  • ISBN-13: 978-1800562882

eBook Description:

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

MLOps is a systematic approach to building, deploying, and monitoring machine learning (ML) 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 ML models, and build ML pipelines to train and deploy models securely in production.

The Engineering MLOps 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.

  • Formulate data governance strategies and pipelines for ML 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

By the end of this Engineering MLOps book, you’ll have a 360-degree view of MLOps and be ready to implement MLOps in your organization.

[ Read also: Cloud-Based MLOps Architecture – AWS, Google, Azure article on The Cloud Navigator ]

[ Read also: Industrializing AI with MLOps: Can it live up to its promise? article on The Cloud Navigator ]


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