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

Exploring the Azure ML Studio experience

Azure Machine Learning comes with a dedicated web interface that allows you to implement both no-code and code-first data science initiatives. You can access the web interface either through the Launch studio button within the Azure portal resource, as you saw in the previous section, or by visiting the https://ml.azure.com page directly. With the latter approach, if this is your first time you've visited the Studio site, you will have to manually select the Azure Active Directory tenant, the Subscription, and the name of the Machine Learning workspace you want to connect to, as shown in Figure 3.3.

Figure 3.3 – Selecting the machine learning workspace in ml.azure.com

Once you've selected your workspace, you will land on the home page of Azure Machine Learning Studio, as shown in Figure 3.4.

Figure 3.4 – Azure Machine Learning Studio home page

On the left-hand side, you...