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

Managing infrastructure resources in Azure ML Studio

To conduct an experiment, you will need a couple of infrastructure resources to consume. You can configure and manage them through the following sections:

  • Compute provides the managed compute infrastructure you can use in your experiments. This allows you to register and utilize virtual machines that may have multiple CPUs and GPUs and memory sizes that can load humongous datasets into them. Having those computes as a managed service means that you don't have to worry about installing the operating system or keeping it patched and up to date. You will learn more about the various compute options in Chapter 4, Configuring the Workspace.
  • Datastores contains the connection information needed to get access to the data within various engines, such as Azure Blob Storage and Azure SQL Database. This information is used to access the datasets that you registered in the Compute section. You will learn more about the concepts...