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

Overview of the designer

AzureML Studio offers a graphical designer that allows you to author pipelines visually. As per the definition, a pipeline is an independently executable flow of subtasks that describes a machine learning task. There are three types of pipelines that you can create within the designer:

  • Training pipelines: These pipelines are used for training models.
  • Batch inference pipelines: These pipelines are used to operationalize pre-trained models for batch prediction.
  • Real-time inference pipelines: These pipelines are used to expose a REST API that allows third-party applications to make real-time predictions using pre-trained models.

To create a batch and a real-time pipeline, you need to author a training pipeline. In the following sections, you will learn how to create a training pipeline and then produce a batch and real-time pipeline on top of it. In Chapter 11, Working with Pipelines, you will learn how to author similar pipelines through...