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Azure Data Scientist Associate Certification Guide: A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam

by Andreas Botsikas (Author), Michael Hlobil (Author)

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Develop the skills you need to run machine learning workloads in Azure and pass the DP-100 exam with ease

Key Features:

Create end-to-end machine learning training pipelines, with or without code
Track experiment progress using the cloud-based MLflow-compatible process of Azure ML services
Operationalize your machine learning models by creating batch and real-time endpoints

Book Description:

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.

What You Will Learn:

Create a working environment for data science workloads on Azure
Run data experiments using Azure Machine Learning services
Create training and inference pipelines using the designer or code
Discover the best model for your dataset using Automated ML
Use hyperparameter tuning to optimize trained models
Deploy, use, and monitor models in production
Interpret the predictions of a trained model

Who this book is for:

This book is for developers who want to infuse their applications with AI capabilities and data scientists looking to scale their machine learning experiments in the Azure cloud. Basic knowledge of Python is needed to follow the code samples used in the book. Some experience in training machine learning models in Python using common frameworks like scikit-learn will help you understand the content more easily.

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Editorial Reviews

Review

"This book stands out as a zero-to-hero guide for implementing ML/AI solutions using Azure Machine Learning Services. Not only does it cover the foundational concepts required for someone who is just getting started with the service, but also provides hands-on material on complex yet important topics like pipelines, operationalizing models with code, and model explainability and fairness. Even as a regular practitioner of AzureML, I found the code samples and concepts insightful."

Varma Gadhiraju, Senior FastTrack Engineer (Azure Data Analytics and Machine Learning) at Microsoft

"As suggested in the title, the main purpose of this book is to help in the preparation towards Azure Data Scientist Associate Certification and it stays true to the title throughout. At the same time, it focuses on providing practical knowledge on machine learning using the Azure platform, making the book indispensable for the exam and also for the journey beyond. The steps provided are clear and concise and are supplemented well with screenshots and example code to guide the reader. The code snippets provided are also simple and easy to understand."

Chandra Mohan, Senior Azure FastTrack Engineer, Microsoft

"This book is very helpful for getting started or wanting to dive deeper into Azure ML topics prior to sitting the Azure Data Scientist exam. It contains not only well-thought-out descriptions but also practical exercises to help get hands-on experience with Azure ML. This ensures that the reader not only gets to know how to do something but also the reasoning and theory behind the why as well."

Elaine van Bergen, Principal Service Engineering Manager, FastTrack for Azure at Microsoft

"This book is a fantastic guide for anyone wishing to learn how to employ machine learning and data science with the Azure ML platform, covering material required for the DP-100 Exam and more. It provides a comprehensive walkthrough, starting with the fundamentals through to deploying models using straightforward and practical examples. All levels of technical depth are covered with examples using no-code, the graphic visual designer, and the Python SDK. I particularly liked the attention to some of the more important, yet often overlooked, aspects of machine learning and data science such as detecting drift on datasets and the Responsible AI toolkit."

Kris Bock, ML Engineer at Microsoft

"This is an excellent book that introduces Azure Data–related concepts in an intuitive and easy-to-follow way. It is very well organized, provides detailed scenarios and solution examples, and can also serve as a reference point on ongoing bases."

Kati Iceva, Principal Group Software Engineering Manager, Microsoft

About the Author

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. 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.

Product details

Publisher ‏ : ‎ Packt Publishing
Publication date ‏ : ‎ December 3, 2021
Language ‏ : ‎ English
Print length ‏ : ‎ 448 pages
ISBN-10 ‏ : ‎ 1800565003
ISBN-13 ‏ : ‎ 978-1800565005
Item Weight ‏ : ‎ 1.68 pounds
Dimensions ‏ : ‎ 7.5 x 1.01 x 9.25 inches
Best Sellers Rank: #2,275,045 in Books (See Top 100 in Books)
- #251 in Microsoft Certification Guides
- #1,001 in Data Processing
- #3,623 in Artificial Intelligence & Semantics
Customer Reviews:
26 ratings

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Michael Hlobil
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Michael Hlobil is an experienced architect focused on quickly understanding customer's 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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Andreas Botsikas
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Full content visible, double tap to read brief content.
Andreas Botsikas is an experienced advisor working in the software industry. He has worked in the finance sector, leading highly efficient DevOps teams, architecting and building high volume transactional systems. He then travelled the world, building AI-infused solutions with a group of data scientists. Currently, he is working 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.
See more on the author's page

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prashant dhanavade
Excellent and well organized
Reviewed in the United States on July 18, 2024
Verified Purchase

Excellent content. Covers all exam topics.

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alejandro
Great book to pass certification
Reviewed in the United States on November 22, 2022
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The book helped me practice and get ready for the certification exam. I recommend until Microsoft rolls out their Python SDK upgrade. After that happens, the book would need to be updated.

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Amit Parulekar
Content outline
Reviewed in the United States on February 20, 2022
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Relevant and well organized content for the DP-100

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Om S
Great resource for Passing Microsoft Certified DP-100 exam
Reviewed in the United States on December 21, 2021
“Azure Data Scientist Associate Certification Guide”: for Microsoft Certified DP-100 exam - The content in the book is prodigious, and the authors have tried to compile all the important DP-100 ML exams-related topics in a medium-sized book.
This book contains relevant hands-on exercises which are easy to follow along and implement with a visual interface, not only The order of the content topics is extremely good as well.
This book covers advanced topics that are required for certification, so prior knowledge of the Azure cloud and basic ML knowledge is a bonus and makes learning easy.

Each chapter has questions and references to “Further reading” which complement the exam topic, which is extremely helpful. The book starts from very basic topics and goes to advance topics as exams require to have an understanding of many topics, starting from overview, understanding Azure ML Studio, AutoML, Spark, Deployment, pipeline batch processing, real-time inference, analyzing model error, AzureML SDK, etc. I found the way topics are explained is fascinating. I would recommend it to anyone who is willing to pass the DP-100 exam and “get hands-on “ in MS Azure ml cloud platform. My recommendation would be to add more questions with answers for practice, this would add more value to the book.

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Patrick Hagan
Screenshots are very small and hard to read.
Reviewed in the United States on November 2, 2022
Some of the Screenshots were OK and I could read the image. Most of them were very small. Even when I expanded the Image, the typing on the Image was still a very small Font.

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kio
Great book to learn Azure ML well and pass DP-100 exam for sure.
Reviewed in the United States on December 13, 2021
I have been working with Andreas Botsikas, Michael Hlobil for more than 2 years now. As a category lead for FastTrack AI team, Andreas is instrumental on how we help our customers with AI solutions on a daily basis. His depth with Azure ML is such that for any ML question, my go to person is Andreas, he is my mentor. This book reflects the knee-deep expertise from Andreas and Michael. I recommend every professional, wanting to get up to speed with Azure ML, to take the journey with Andreas and Michael, as they take us through the core concepts of Azure ML, with examples, so you can become successful operationalizing your ML solution with Azure ML.

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kio

Great book to learn Azure ML well and pass DP-100 exam for sure.

Reviewed in the United States on December 13, 2021
I have been working with Andreas Botsikas, Michael Hlobil for more than 2 years now. As a category lead for FastTrack AI team, Andreas is instrumental on how we help our customers with AI solutions on a daily basis. His depth with Azure ML is such that for any ML question, my go to person is Andreas, he is my mentor. This book reflects the knee-deep expertise from Andreas and Michael. I recommend every professional, wanting to get up to speed with Azure ML, to take the journey with Andreas and Michael, as they take us through the core concepts of Azure ML, with examples, so you can become successful operationalizing your ML solution with Azure ML.

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Josh Luedeman
Great certification readiness resource
Reviewed in the United States on December 26, 2021
This book is a great resource for anyone looking to prep for, and pass, the DP 100 exam. The explanations are well thought out and straightforward, and the repository of follow-on material is invaluable. I recommend this resource for anyone looking at attempting this exam.

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Pranav Jain
Innovative book to prepare for Azure Data Science Certification
Reviewed in the United States on December 15, 2021
I found this book to have a very interesting and innovative way of presenting all the topics needed to prepare for Azure Data Science Associate Certification. I've worked on Azure cloud technologies for last 3yrs and have an intermediate knowledge so I wanted to improve my overall knowledge and also prepare for the Certification hence I chose this book as it has a very detailed & easy way of explaining all the complex topics like Visual ML Model training, Optimization of Model, Understanding Model Results & Creating AzureML Pipelines. They also provide lots of code snippets and examples for better understanding and the only thing I found missing was how we can deploy these various models in a Production environment with CI/CD capabilities hence I would rate this a 4/5.

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Top reviews from other countries

Amazon カスタマー
Well explained book
Reviewed in Japan on April 9, 2025
Verified Purchase

This book provides a well-structured and comprehensive explanation of the knowledge required for the Azure Machine Learning certificate exam, covering everything you need without unnecessary fluff.

The inclusion of practical examples is a significant strength, allowing for hands-on learning that really helps solidify understanding. This is particularly crucial for the Azure DP-100 exam, which tests coding skills using the Azure ML Python SDK. Being able to actively work through examples is a big plus.

Furthermore, unlike the official learning materials for Azure DP-100, which can be quite sparse and make comprehension challenging, this book offers abundant explanations that greatly aided my understanding.

However, there is one notable drawback: the version of the Azure ML Python SDK used in the sample code is outdated, meaning the syntax differs from what you might encounter in the actual exam. Therefore, I highly recommend that anyone using this book also refer to the official learning materials to familiarize themselves with the latest sample code.

Taking this flaw into account, I've deducted one star from my overall rating, resulting in a score of 4 out of 5.

P.S.
The Azure DP-100 exam underwent a significant update in January 2025, changing the exam content. Key changes include the removal of questions on ML Designer and the addition of content related to Azure AI Foundry, a suite of Azure's generative AI services. With AI Foundry accounting for approximately 25% of the exam, neglecting this area would be a significant oversight. A new edition of this book to reflect these changes would be highly valuable.

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