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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

Concepts

Machine Learning

Author (1):

Emmanuel Raj

Table of Contents (18) Chapters

Preface

1. Section 1: Framework for Building Machine Learning Models

2. Chapter 1: Fundamentals of an MLOps Workflow

3. Chapter 2: Characterizing Your Machine Learning Problem

4. Chapter 3: Code Meets Data

5. Chapter 4: Machine Learning Pipelines

6. Chapter 5: Model Evaluation and Packaging

7. Section 2: Deploying Machine Learning Models at Scale

8. Chapter 6: Key Principles for Deploying Your ML System

9. Chapter 7: Building Robust CI/CD Pipelines

10. Chapter 8: APIs and Microservice Management

11. Chapter 9: Testing and Securing Your ML Solution

12. Chapter 10: Essentials of Production Release

13. Section 3: Monitoring Machine Learning Models in Production

14. Chapter 11: Key Principles for Monitoring Your ML System

15. Chapter 12: Model Serving and Monitoring

16. Chapter 13: Governing the ML System for Continual Learning

17. Other Books You May Enjoy

Pipeline execution triggers

In an effective CI/CD pipeline, process execution should be possible by means of multiple events or triggers. Having the option to trigger the pipeline by only regular events, such as code repository or push-or-pull requests, might be a handicap or limitation for the system. Having the option to trigger the pipeline process using multiple events enhances the flexibility and functionality of the CI/CD pipeline. Let's look at some types of triggers that can add value to the CI/CD pipeline process:

Artifactory triggers
Artifacts are generated at different stages in the pipeline and development process. Generated artifacts, such as a trained model, metadata, uploaded Docker images, or any file that has been uploaded, can be triggered to execute a certain process in the CI/CD pipeline. Having such options can enable great flexibility and functionality for the CI/CD pipeline.
Docker Hub triggers
Every time you push a new Docker image to a Docker...

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Authors (1)

Emmanuel Raj

Emmanuel Raj is a Finland-based Senior Machine Learning Engineer with 6+ years of industry experience. He is also a Machine Learning Engineer at TietoEvry and a Member of the European AI Alliance at the European Commission. He is passionate about democratizing AI and bringing research and academia to industry. He holds a Master of Engineering degree in Big Data Analytics from Arcada University of Applied Sciences. He has a keen interest in R&D in technologies such as Edge AI, Blockchain, NLP, MLOps and Robotics. He believes "the best way to learn is to teach", he is passionate about sharing and learning new technologies with others.

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