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

Publishing a pipeline to expose it as an endpoint

So far, you have defined a pipeline using the AzureML SDK. If you had to restart the kernel of your Jupyter notebook, you would lose the reference to the pipeline you defined, and you would have to rerun all the cells to recreate the pipeline object. The AzureML SDK allows you to publish a pipeline that effectively registers it as a versioned object within the workspace. Once a pipeline is published, it can be submitted without the Python code that constructed it.

In a new cell in your notebook, add the following code:

published_pipeline = pipeline.publish(
    "Loans training pipeline", 
    description="A pipeline to train a LightGBM model")

This code publishes the pipeline and returns a PublishedPipeline object, the versioned object registered within the workspace. The most interesting attribute of that object is the endpoint, which returns the REST endpoint URL...