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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
Author (1):
Emmanuel Raj Emmanuel Raj
Profile icon Emmanuel Raj
LinkedIn
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.
See other products by 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

Pipeline release management

Releases in the CI/CD pipelines allow your team to automate fully and continuously deliver software to your customers faster and with lower risk. Releases allow you to test and deliver your software in multiple stages of production or set up semi-automated processes with approvals and on-demand deployments. It is vital to monitor and manage these releases. We can manage releases by accessing the pipeline from Pipelines | Releases and selecting our CI/CD pipeline (for example, Port Weather ML Pipeline), as shown in the following screenshot:

Figure 10.16 – Pipeline Release Management

Here, you can keep track of all the releases and their history and perform operations for each release, such as redeploying, abandoning, checking logs, and so on. You can see the releases shown in the following screenshot. By clicking on individual releases (for example, Release 4), we can check which model and artifacts were deployed in the release...

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Authors (1)

Profile icon Emmanuel Raj
LinkedIn
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.
Read more
See other products by Emmanuel Raj

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