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

Integrating pipelines with other Azure services

It's rare that users use only a single service to manage data flows, experimentation, training, deployment, and CI/CD in the cloud. Other services provide specific benefits that make them a better fit for certain tasks, such as Azure Data Factory for loading data into Azure, as well as Azure Pipelines for CI/CD and running automated tasks in Azure DevOps.

The strongest argument for betting on a cloud provider is strong integration with the individual services. In this section, we will see how Azure Machine Learning pipelines integrate with other Azure services. The list for this section would be a lot longer if we were to cover every possible service for integration. As we learned in this chapter, you can trigger a published pipeline by calling a REST endpoint, and you can submit a pipeline using standard Python code. This means you can integrate pipelines anywhere where you can call HTTP endpoints or run Python code.

We will...