Wow! eBook
Azure Data Scientist Associate Certification Guide: A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam
Author(s): Andreas Botsikas (Author), Michael Hlobil (Author)
- Publisher: Packt Publishing
- Publication Date: 3 Dec. 2021
- Language: English
- Print length: 448 pages
- ISBN-10: 1800565003
- ISBN-13: 9781800565005
Book Description
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.
Table of Contents
- An Overview of Modern Data Science
- Deploying Azure Machine Learning Workspace Resources
- Azure Machine Learning Studio Components
- Configuring the Workspace
- Letting the Machines Do the Model Training
- Visual Model Training and Publishing
- The AzureML Python SDK
- Experimenting with Python Code
- Optimizing the ML Model
- Understanding Model Results
- Working with Pipelines
- Operationalizing Models with Code
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
