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

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.

Environments...