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

Demystifying the different Azure services for ML

Azure offers many services that can be used to perform ML – you can use a simple Virtual Machine (VM), a pre-configured VM for ML (also called Data Science Virtual Machine (DSVM)), Azure Notebooks using a shared free kernel, or any other service that gives you compute resources and data storage. Due to this flexibility, it is often very difficult to navigate through these services and pick the correct service for implementing an ML pipeline. In this section, we will provide clear guidance about how to choose the optimal ML and compute services in Azure.

First, it is important to discuss the difference between a simple compute resource, an ML infrastructure service, and an ML modeling service. This distinction will help you to better understand the following sections about how to choose these services for a specific use case: