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

Summary

In this chapter, you learned about the origins of data science and how it relates to machine learning. You then learned about the iterative nature of a data science project and discovered the various phases you will be working on. Starting from the problem understanding phase, you will then acquire and explore data, create new features, train a model, and then deploy to verify your hypothesis. Then, you saw how you can scale out the processing of big data files using the Spark ecosystem. In the last section, you discovered the DevOps mindset that helps agile teams be more efficient, meaning that they develop and deploy new product features in short periods of time. You saw the components that are commonly used within an MLOps-driven team, and you saw that in the epicenter of that diagram, you find AzureML.

In the next chapter, you will learn how to deploy an AzureML workspace and understand the Azure resources that you will be using in your data science journey throughout this book.