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Azure Data Scientist Associate Certification Guide

You're reading from   Azure Data Scientist Associate Certification Guide A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781800565005
Length 448 pages
Edition 1st Edition
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Authors (2):
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Andreas Botsikas Andreas Botsikas
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Andreas Botsikas
Michael Hlobil Michael Hlobil
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Michael Hlobil
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Starting your cloud-based data science journey
2. Chapter 1: An Overview of Modern Data Science FREE CHAPTER 3. Chapter 2: Deploying Azure Machine Learning Workspace Resources 4. Chapter 3: Azure Machine Learning Studio Components 5. Chapter 4: Configuring the Workspace 6. Section 2: No code data science experimentation
7. Chapter 5: Letting the Machines Do the Model Training 8. Chapter 6: Visual Model Training and Publishing 9. Section 3: Advanced data science tooling and capabilities
10. Chapter 7: The AzureML Python SDK 11. Chapter 8: Experimenting with Python Code 12. Chapter 9: Optimizing the ML Model 13. Chapter 10: Understanding Model Results 14. Chapter 11: Working with Pipelines 15. Chapter 12: Operationalizing Models with Code 16. Other Books You May Enjoy

Scheduling a recurring pipeline

Being able to invoke a pipeline through the published REST endpoint is great when you have third-party systems that need to invoke a training process after a specific event has occurred. For example, suppose you are using Azure Data Factory to copy data from your on-premises databases. You could use the Machine Learning Execute Pipeline activity and trigger a published pipeline, as shown in Figure 11.9:

Figure 11.9 – Sample Azure Data Factory pipeline triggering an AzureML published pipeline following a copy activity

If you wanted to schedule the pipeline to be triggered monthly, you would need to publish the pipeline as you did in the previous section, get the published pipeline ID, create a ScheduleRecurrence, and then create the Schedule. Return to your notebook where you already have a reference to published_pipeline. Add a new cell with the following code:

from azureml.pipeline.core.schedule import ScheduleRecurrence...
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