Azure Data Scientist Associate Certification Guide : A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam
Language: English
Published by Packt Publishing, 2021
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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nach der Bestellung gedruckt Neuware - Printed after ordering - Develop the skills you need to run machine learning workloads in Azure and pass the DP-100 exam with easeKey Features:Create end-to-end machine learning training pipelines, with or without codeTrack experiment progress using the cloud-based MLflow-compatible process of Azure ML servicesOperationalize your machine learning models by creating batch and real-time endpointsBook 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 AzureRun data experiments using Azure Machine Learning servicesCreate training and inference pipelines using the designer or codeDiscover the best model for your dataset using Automated MLUse hyperparameter tuning to optimize trained modelsDeploy, use, and monitor models in productionInterpret the predictions of a trained modelWho 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. …
Seller Inventory # 9781800565005
- Title
- Azure Data Scientist Associate Certification Guide : A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam
- Author
- Michael Hlobil
- Publisher
- Packt Publishing
- Publication year
- 2021
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1800565003
- ISBN 13
- 9781800565005
- Item weight
- 831 grams
- Dimensions
- 235x191x25 mm
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
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"About the title" may belong to another edition of this title.
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