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

Basic coding with the AzureML SDK

The first class you will work with is the AzureML Workspace, a class that gives you access to all the resources within your workspace. To create a reference to your workspace, you will need the following information:

  • Subscription ID: The subscription where the workspace is located. This is a Globally Unique Identifier (GUID, also known as a UUID) that consists of 32 hexadecimal (0-F) digits; for example, ab05ab05-ab05-ab05-ab05-ab05ab05ab05. You can find this ID in the Azure portal in the Properties tab of the subscription you are using.
  • Resource group name: The resource group that contains the AzureML workspace components.
  • Workspace name: The name of the AzureML workspace.

You can store this information in variables by running the following assignments:

subscription_id = '<Subscription Id>'
resource_group = 'packt-azureml-rg'
workspace_name = 'packt-learning-mlw'

The first approach to...