Exam Ref DP-100 Designing and Implementing a Data Science Solution on Azure
Addison Wesley (Verlag)
978-0-13-687179-8 (ISBN)
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Set up an Azure Machine Learning workspace
Run experiments and train models
Optimize and manage models
Deploy and consume models
This Microsoft Exam Ref:
Organizes its coverage by exam objectives
Features strategic, what-if scenarios to challenge you
Assumes you are a business user, IT professional, or student interested in cloud computing and technologies, including individuals planning to pursue more advanced Microsoft 365 certification
About the Exam Exam DP-100 focuses on knowledge needed to apply scientific rigor and data exploration techniques to gain actionable insights and communicate results to stakeholders; use machine learning techniques to train, evaluate, and deploy models to build AI solutions that satisfy business objectives; use applications that involve natural language processing, speech, computer vision, and predictive analytics.
About Microsoft Certification Passing this exam fulfills your requirements for the Microsoft Certified: Azure Data Scientist Associate credential, demonstrating that you understand how to implement and run machine learning workloads on Microsoft Azure; in particular, using Azure Machine Learning Service.
See full details at: www.microsoft.com/learn
Stefano Tucci is a Developer and Data Analytics Consultant. He is born in Italy and has a strong DB and BI background as well as a special interest in scripting languages like SQL, U-SQL, R, Python, C#, .NET, HTML, JS, and CSS. He has a Bachelor’s degree in Economics and Management and a Master’s degree in IT Security and Computer Forensics. He is currently studying for a fourth degree in Computer Engineering. He works in the IT department of an international company. Stefano is enthusiastic about technology, especially Microsoft technology.
Chapter 1 Set up an Azure Machine Learning workspace Create an Azure Machine Learning workspace Manage data objects in an Azure Machine Learning workspace Manage experiment compute contexts
Chapter 2 Run experiments and train models Create models by using Azure Machine Learning Designer Run training scripts in an Azure Machine Learning workspace Generate metrics from an experiment run Automate the model training process
Chapter 3 Optimize and manage models Use Automated ML to create optimal models Use Hyperdrive to rune hyperparameters Use model explainers to interpret models Manage models
Chapter 4 Deploy and consume models Create production compute targets Deploy a model as a service Create a pipeline for batch inferencing Publish a Designer pipeline as a web service
| Erscheinungsdatum | 06.04.2021 |
|---|---|
| Reihe/Serie | Exam Ref |
| Verlagsort | Boston |
| Sprache | englisch |
| Themenwelt | Informatik ► Weitere Themen ► Zertifizierung |
| ISBN-10 | 0-13-687179-8 / 0136871798 |
| ISBN-13 | 978-0-13-687179-8 / 9780136871798 |
| Zustand | Neuware |
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