You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800562882

Length 370 pages

Edition 1st Edition

Tools

Azure Functions

Concepts

Machine Learning

Author (1):

Emmanuel Raj

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Table of Contents (18) Chapters

Preface

1. Section 1: Framework for Building Machine Learning Models

2. Chapter 1: Fundamentals of an MLOps Workflow FREE CHAPTER

3. Chapter 2: Characterizing Your Machine Learning Problem

4. Chapter 3: Code Meets Data

5. Chapter 4: Machine Learning Pipelines

6. Chapter 5: Model Evaluation and Packaging

7. Section 2: Deploying Machine Learning Models at Scale

8. Chapter 6: Key Principles for Deploying Your ML System

9. Chapter 7: Building Robust CI/CD Pipelines

10. Chapter 8: APIs and Microservice Management

11. Chapter 9: Testing and Securing Your ML Solution

12. Chapter 10: Essentials of Production Release

13. Section 3: Monitoring Machine Learning Models in Production

14. Chapter 11: Key Principles for Monitoring Your ML System

15. Chapter 12: Model Serving and Monitoring

16. Chapter 13: Governing the ML System for Continual Learning

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Setting up a CI/CD pipeline and the test environment (using Azure DevOps)

In the previous section, we went through the theory of CI, continuous delivery, and continuous deployment, and now it is time to see it in practice. Using Azure DevOps, we will set up a simple CI/CD pipeline of our own for the business problem (weather prediction), which we have been working on previously (in Chapter 6, Key Principles for Deploying Your ML System, in the Hands-on deployment section (for the business problem)).

Azure DevOps is a service provided by Microsoft that facilitates source code management (version control), project management, CI, continuous delivery, and continuous deployment (automated builds, testing, and release capabilities). It also enables life cycle management for software applications. We will use Azure DevOps for hands-on training as it comes with seamless integration with the Azure ML service, which we have been using previously in Chapter 6. You will experience the integration...

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You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Table of Contents (18) Chapters

Setting up a CI/CD pipeline and the test environment (using Azure DevOps)

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You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Table of Contents (18) Chapters

Setting up a CI/CD pipeline and the test environment (using Azure DevOps)

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