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

17. Other Books You May Enjoy

Pipeline execution and testing

Now, it is time to test your pipeline and for that we will create a release and validate whether the pipeline release has executed successfully. The following steps will help you to test your pipeline:

Click on the Create release button to execute jobs configured on your pipeline. A popup will appear on the right of your screen (as shown in Figure 7.16) to view and select artifacts to deploy in your staging environment.
Select the artifacts (_scaler and _support-vector-classifier) and select their versions. For simplicity, version 1 is recommended for both.
If you want to choose another version of your model or scaler make sure to change the path of your model and scaler in the score.py file (that is, insert the appropriate version number in the scaler and model paths model-scaler/{version number}/modelscaler.pkl and support-vector-classifier/ {version number} /svc.onnx. If you choose version 1, you don't have to worry about changing...

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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

Pipeline execution and testing

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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

Pipeline execution and testing

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