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Engineering MLOps

You're reading from  Engineering MLOps

Product type Book
Published in Apr 2021
Publisher Packt
ISBN-13 9781800562882
Pages 370 pages
Edition 1st Edition
Languages
Author (1):
Emmanuel Raj Emmanuel Raj
Profile icon Emmanuel Raj

Table of Contents (18) Chapters

Preface Section 1: Framework for Building Machine Learning Models
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 Other Books You May Enjoy

Enabling continuous monitoring for the service

The Explainable Monitoring Framework can be resourceful if we wish to monitor ML systems in production. In the next chapter, we will enable the Explainable Monitoring Framework for the business use case we worked on in the previous chapters. We will enable continuous monitoring for the system we have deployed. We will then monitor the ML application that's been deployed to production and analyze the incoming data and the model's performance to govern the ML system to produce maximum business value for the use case.

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