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

Connecting to datastores

Datastores are the engines where your data resides and provide access to anyone authorized to do so. In most Python examples you see on the internet, there is a connection string that contains the credentials to connect to a database or a blob store. There are a couple of drawbacks associated with this technique:

  • The credentials stored within these scripts are considered a security violation, and you can accidentally expose your protected datasets by publishing a script in a public repository such as GitHub.
  • You need to manually update all the scripts when the credentials change.

Azure ML allows you to have a single centralized location where you define the connection properties to various stores. Your credentials are securely stored as secrets within the workspace's associated key vault. In your scripts, you reference the datastore using its name and you can access its data without having to specify the credentials. If, at some point...