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

Summary

In this chapter, you learned how to configure an AutoML process to discover the best model that can predict whether a customer will churn or not. First, you used the AutoML wizard of the Azure Machine Learning Studio web experience to configure the experiment. Then, you monitored the execution of the run in the Experiments section of the studio interface. Once the training was completed, you reviewed the trained models and saw the information that had been stored regarding the best model. Then, you deployed that machine learning model in an Azure Container Instance and tested that the real-time endpoint performs the requested inferences. In the end, you deleted the deployment to avoid incurring costs in your Azure subscription.

In the next chapter, you will continue exploring the no-code/low code aspects of the Azure Machine Learning Studio experience by looking at the designer, which allows you to graphically design a training pipeline and operationalize the produced model...