You're reading from Mastering Azure Machine Learning Perform large-scale end-to-end advanced machine learning in the cloud with Microsoft Azure Machine Learning

Product type Paperback

Published in Apr 2020

Publisher Packt

ISBN-13 9781789807554

Length 436 pages

Edition 1st Edition

Languages

Tools

Azure

Concepts

Machine Learning

Authors (2):

Christoph Körner

Kaijisse Waaijer

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Table of Contents (20) Chapters

Preface

About Mastering Azure Machine Learning

Section 1: Azure Machine Learning

1. Building an end-to-end machine learning pipeline in Azure FREE CHAPTER

2. Choosing a machine learning service in Azure

Section 2: Experimentation and Data Preparation

3. Data experimentation and visualization using Azure

4. ETL, data preparation, and feature extraction

5. Azure Machine Learning pipelines

6. Advanced feature extraction with NLP

Section 3: Training Machine Learning Models

7. Building ML models using Azure Machine Learning

8. Training deep neural networks on Azure

9. Hyperparameter tuning and Automated Machine Learning

10. Distributed machine learning on Azure

11. Building a recommendation engine in Azure

Section 4: Optimization and Deployment of Machine Learning Models

12. Deploying and operating machine learning models

13. MLOps—DevOps for machine learning

14. What's next?

Index

Using distributed ML in Azure

The Exploring methods for distributed ML section contained an overwhelming amount of different parallelization scenarios, various communication backends for collective algorithms, and code examples using different ML frameworks and even execution engines. The amount of choice when it comes to ML frameworks is quite large and making an educated decision is not easy. This choice gets even more complicated when some frameworks are supported out of the box in Azure Machine Learning while others have to be installed, configured, and managed by the user.

In this section, we will go through the most common scenarios, learn how to choose the correct combination of frameworks, and implement a distributed ML pipeline in Azure.

In general, you have three choices for running distributed ML in Azure:

The first obvious choice is using Azure Machine Learning, the Notebook environment, the Azure Machine Learning SDK, and Azure Machine Learning compute clusters...

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You're reading from Mastering Azure Machine Learning Perform large-scale end-to-end advanced machine learning in the cloud with Microsoft Azure Machine Learning

Table of Contents (20) Chapters

Using distributed ML in Azure

Authors (2)

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You're reading from Mastering Azure Machine Learning Perform large-scale end-to-end advanced machine learning in the cloud with Microsoft Azure Machine Learning

Table of Contents (20) Chapters

Using distributed ML in Azure

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Authors (2)

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