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Mastering Azure Machine Learning

You're reading from Mastering Azure Machine Learning

Product type Book

Published in Apr 2020

Publisher Packt

ISBN-13 9781789807554

Pages 436 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Authors (2):

Christoph Körner

Kaijisse Waaijer

View More author details

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

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...

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Authors (2)

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.

See other products by Christoph Körner

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.

See other products by Kaijisse Waaijer

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