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

Provisioning compute resources

Compute resources allow you to execute code scripts during your data exploratory analysis, the training phase, and when operationalizing ML models. The Azure ML workspace offers the following types of compute resources:

  • Compute instances: These are virtual machines dedicated to each data scientist that is working in the Azure ML workspace.
  • Compute clusters: These are scalable computer clusters that can run multiple training or inference steps in parallel.
  • Inference clusters: These are Azure Kubernetes Service (AKS) clusters that can operationalize Docker images, which expose your models through a REST API.
  • Attached compute: These are existing compute resources, such as Ubuntu Virtual Machines (VMs) or Synapse Spark pools, that can be attached to the workspace to execute some of the steps of your training or inference pipelines.

When you visit the Manage | Compute section of Azure ML Studio, you will see and be able to manage...