Book Image

Azure Data Scientist Associate Certification Guide

By : Andreas Botsikas, Michael Hlobil
Book Image

Azure Data Scientist Associate Certification Guide

By: Andreas Botsikas, Michael Hlobil

Overview of this book

The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam.
Table of Contents (17 chapters)
1
Section 1: Starting your cloud-based data science journey
6
Section 2: No code data science experimentation
9
Section 3: Advanced data science tooling and capabilities

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...