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

Overview of the Python SDK

The AzureML SDK is a Python library that allows you to interact with the AzureML services. It also provides you with data science modules that will assist you in your machine learning journey. The AzureML SDK is available in the R programming language through a Python to R interoperability package.

The SDK consists of several packages that group different types of modules you can import into your code base. All the Microsoft-supported modules are placed within packages that start with azureml, such as azureml.core and azureml.train.hyperdrive. The following diagram offers a broad overview of the AzureML SDK's most frequently used packages, as well as the key modules that you will see in this book and the exam:

Figure 7.1 – The AzureML SDK modules and important classes

Note that all the key classes that exist in the azureml.core package can also be imported from the corresponding child module. For example, the Experiment...