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)
Section 1: Starting your cloud-based data science journey
Section 2: No code data science experimentation
Section 3: Advanced data science tooling and capabilities

Interacting with the Azure ML resource

In the previous chapter, you deployed the packt-learning-mlw machine learning resource within the packt-azureml-rg resource group. Navigate to the deployed resource by typing in its name in the top search bar and selecting the resource from the results list:

Figure 3.1 – Navigating to the Azure Machine Learning resource

This will land you on the overview pane of the resource, as shown in Figure 3.2:

  1. On the left-hand side, you will see the typical resource menu that most of the Azure services have. This menu is also referred to as the left pane.
  2. At the top, you will see the command bar, which allows you to download the config.json file, a file that contains all the information you need to connect to the workspace through the Python SDK, and to delete the machine learning workspace.
  3. Below the command bar, you can see the working pane, which is where you can view information related to the workspace...