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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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Benefits of pipelines for ML workflows

Separating your workflow into reusable configurable steps and combining these steps to form an end-to-end pipeline provides many benefits for implementing end-to-end ML processes. Multiple teams can own and iterate on individual steps to improve the pipeline over time, while others can easily integrate each version of the pipeline into their current setup.

The pipeline itself doesn't only split code from execution—it also splits the execution from the orchestration. Hence, you can configure individual compute targets that can be used to optimize your execution and provide parallel execution, during which you don't have to touch the ML code.

We will take a quick look into Azure Machine Learning pipelines and why they should be your tool of choice when implementing ML workflows in Azure. In the following section, Building and publishing an ML pipeline, we will dive a lot deeper and explore the individual features by building...

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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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