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

Working with datasets

In the previous sections, you were configuring compute and datastore resources under the Manage section of the studio. With this infrastructure configured, you can start pulling data into your registered datastores and register datasets in the Assets section of the studio:

Figure 4.41 – Datasets in the Assets section of the Azure ML Studio experience

Datasets is an abstraction layer on top of the data that you are using for training and inference. It contains a reference to the physical data's location and provides a series of metadata that can help you understand their shape and statistical properties. When you want to access the dataset, you can reference it via its name, and you don't have to worry about credentials or exact file paths. Moreover, all the data scientists working on the same workspace can access the same datasets, allowing them to experiment on the same data in parallel.

There are two types of datasets...