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

Technical requirements

You will need to have access to an Azure subscription. Within that subscription, you will need a resource group named packt-azureml-rg. You will need to have either a Contributor or Owner Access control (IAM) role at the resource group level. Within that resource group, you should have already deployed a machine learning resource named packt-learning-mlw, as described in Chapter 2, Deploying Azure Machine Learning Workspace Resources.

You will also need to have a basic understanding of the Python language. The code snippets in this chapter target Python version 3.6 or later. You should know the basics of how a Jupyter notebook works and how the variables that you defined in one cell exist in the execution context of others.

You can find all the notebooks and code snippets for this chapter on GitHub at http://bit.ly/dp100-ch07.