Azure Data Scientist Associate Certification Guide

By Andreas Botsikas , Michael Hlobil
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    Chapter 1: An Overview of Modern Data Science
About 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.

Publication date:
December 2021


Section 1: Starting your cloud-based data science journey

An Azure Data Scientist Associate is a subject matter expert with applied data science and machine learning knowledge, able to run machine learning workloads on Azure. The responsibilities for this role include planning and creating a suitable working environment for data science workloads on Azure. In this section, you will get an overview of the data science and machine learning terminology used throughout the book. You will also learn how to provision and configure the Azure Machine Learning services, the de facto environment for running machine learning experiments in the Azure cloud.

This section comprises of the following chapters:

  • Chapter 1, An Overview of Modern Data Science
  • Chapter 2, Deploying Azure Machine Learning Workspace Resources
  • Chapter 3, Azure Machine Learning Studio Components
  • Chapter 4, Configuring the Workspace
About the Authors
  • Andreas Botsikas

    Andreas Botsikas is an experienced advisor working in the software industry. He has worked in the finance sector, leading highly efficient DevOps teams, and architecting and building high-volume transactional systems. He then traveled the world, building AI-infused solutions with a group of engineers and data scientists. Currently, he works as a trusted advisor for customers onboarding into Azure, de-risking and accelerating their cloud journey. He is a strong engineering professional with a Doctor of Philosophy (Ph.D.) in resource optimization with artificial intelligence from the National Technical University of Athens.

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  • Michael Hlobil

    Michael Hlobil is an experienced architect focused on quickly understanding customers' business needs, with over 25 years of experience in IT pitfalls and successful projects, and is dedicated to creating solutions based on the Microsoft Platform. He has an MBA in Computer Science and Economics (from the Technical University and the University of Vienna) and an MSc (from the ESBA) in Systemic Coaching. He was working on advanced analytics projects in the last decade, including massive parallel systems and Machine Learning systems. He enjoys working with customers and supporting the journey to the cloud.

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