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You're reading from  Mastering Azure Machine Learning

Product typeBook
Published inApr 2020
Reading LevelBeginner
PublisherPackt
ISBN-139781789807554
Edition1st Edition
Languages
C#
Tools
Azure
Concepts
Machine Learning
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Authors (2):
Christoph Körner
Christoph Körner
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Christoph Körner

Christoph Körner previously worked as a cloud solution architect for Microsoft, specializing in Azure-based big data and machine learning solutions, where he was responsible for designing end-to-end machine learning and data science platforms. He currently works for a large cloud provider on highly scalable distributed in-memory database services. Christoph has authored four books: Deep Learning in the Browser for Bleeding Edge Press, as well as Mastering Azure Machine Learning (first edition), Learning Responsive Data Visualization, and Data Visualization with D3 and AngularJS for Packt Publishing.
Read more about Christoph Körner

Kaijisse Waaijer
Kaijisse Waaijer
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Kaijisse Waaijer

Kaijisse Waaijer is an experienced technologist specializing in data platforms, machine learning, and the Internet of Things. Kaijisse currently works for Microsoft EMEA as a data platform consultant specializing in data science, machine learning, and big data. She works constantly with customers across multiple industries as their trusted tech advisor, helping them optimize their organizational data to create better outcomes and business insights that drive value using Microsoft technologies. Her true passion lies within the trading systems automation and applying deep learning and neural networks to achieve advanced levels of prediction and automation.
Read more about Kaijisse Waaijer

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Summary

In this chapter, we introduced MLOps, a DevOps-like workflow for developing, deploying, and operating ML services. DevOps aims to provide a quick and high-quality way of making changes to code and deploying these changes to production.

We first learned that Azure DevOps gives us all the features to run powerful CI/CD pipelines. We can run either build pipelines, where steps are coded in YAML, or release pipelines, which are configured in the UI. Release pipelines can have manual or multiple automatic triggers—for example, a commit in the version control repository or if the artifact of a model registry was updated—and creates an output artifact for release or deployment.

Version-controlling your code is necessary, but it's not enough to run proper CI/CD pipelines. In order to create reproducible builds, we need to make sure that the dataset is also versioned and that pseudo-random generators are seeded with a specified parameter.

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Mastering Azure Machine Learning
Published in: Apr 2020Publisher: PacktISBN-13: 9781789807554
© 2020 Packt Publishing Limited All Rights Reserved

Authors (2)

author image
Christoph Körner

Christoph Körner previously worked as a cloud solution architect for Microsoft, specializing in Azure-based big data and machine learning solutions, where he was responsible for designing end-to-end machine learning and data science platforms. He currently works for a large cloud provider on highly scalable distributed in-memory database services. Christoph has authored four books: Deep Learning in the Browser for Bleeding Edge Press, as well as Mastering Azure Machine Learning (first edition), Learning Responsive Data Visualization, and Data Visualization with D3 and AngularJS for Packt Publishing.
Read more about Christoph Körner

author image
Kaijisse Waaijer

Kaijisse Waaijer is an experienced technologist specializing in data platforms, machine learning, and the Internet of Things. Kaijisse currently works for Microsoft EMEA as a data platform consultant specializing in data science, machine learning, and big data. She works constantly with customers across multiple industries as their trusted tech advisor, helping them optimize their organizational data to create better outcomes and business insights that drive value using Microsoft technologies. Her true passion lies within the trading systems automation and applying deep learning and neural networks to achieve advanced levels of prediction and automation.
Read more about Kaijisse Waaijer

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