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

Data registration and versioning

It is vital to register and version the data in the workspace before starting ML training as it enables us to backtrack our experiments or ML models to the source of data used for training the models. The purpose of versioning the data is to backtrack at any point, to replicate a model's training, or to explain the workings of the model as per the inference or testing data for explaining the ML model. For these reasons, we will register the processed data and version it to use it for our ML pipeline. We will register and version the processed data to the Azure Machine Learning workspace using the Azure Machine Learning SDK as follows:

subscription_id = '---insert your subscription ID here----'
resource_group = 'Learn_MLOps'
workspace_name = 'MLOps_WS' 
workspace = Workspace(subscription_id, resource_group, workspace_name)

Fetch your subscription ID, resource_group and workspace_name from the Azure...