Richard Young

Azure Stack Hub Demystified (eBook, ePUB)

Building hybrid cloud, IaaS, and PaaS solutions

• Format: ePub

Produktbeschreibung

Azure Stack Hub is the on-premise offering from Microsoft, which provides Azure Cloud services within a customer's own data center. It provides consistent processes between on-site and the cloud, allowing developers to test locally and deploy to the cloud in exactly the same manner. Azure Stack Hub Demystified provides complete coverage of deploying, configuring, administrating, and running Microsoft Azure Stack Hub efficiently.
Firstly, you will learn how to deploy Azure Stack Hub within an organization. As you progress, you'll understand configuration and the different services provided by the platform. The book also focuses on the underlying architecture and connectivity options for the modern data center. Later, you will understand various approaches to DevOps and their implementation, and learn key topics for the AZ-600 exam.
By the end of this Azure book, you will have a thorough understanding of Azure Stack Hub and the services that are provided by the platform, along with the confidence and information you need to be able to pass the AZ-600 exam.

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Produktdetails

• Verlag: Packt Publishing
• Seitenzahl: 414
• Erscheinungstermin: 7. Oktober 2021
• Englisch
• ISBN-13: 9781801075978
• Artikelnr.: 70296242

Autorenporträt

Richard A. Young is President of Driving Safety Consulting, LLC, in Troy, Michigan. He has published over 70 papers, 17 with SAE. He is active on the following SAE committees: Safety and Human Factors Standards Steering Committee; Driver Metrics, Performance, Behaviors and States; Driver Vehicle Interface; Guidelines for Voice Interaction in Driver Vehicle Interface; and Driving Automation Systems. Until June 2016, he was a Research Professor of Psychiatry and Cognitive Neuroscience at Wayne State University in Detroit, Michigan, with a joint appointment in the School of Medicine and the College of Engineering. He assumed those positions after retiring from General Motors (GM) in 2009 after 25 years.Dr. Young received his PhD degree in experimental and physiological psychology with highest honors from New York University. His PhD thesis focused on human color perception, with Professor Edgar E. Coons as Thesis Advisor and Professor Leon Festinger as Research Advisor. He then received a Harvard Fellow award to investigate the neural mechanisms of learning with Professor Richard Thompson. He next accepted a Postdoctoral Fellowship at the University of California at Berkeley with Professor Russell De Valois. AtBerkeley, Dr. Young did single-unit recordings in the monkey's visual system to investigate the neural mechanisms underlying color and form vision. He continued that research at the University of Oregon with a joint research appointment in the Psychology and Biology Departments. Dr. Young then accepted an offer from Apple Computer in technical support, where he supported Steve Jobs in the world press introduction of the Lisa computer in 1984. He then joined GM in the Computer Science Department at GM R&D, where he developed robot vision systems for 15 years based on his knowledge of the neural mechanisms of human vision. Next, he transferred to the Human Factors Department in GM Engineering, where he stayed for ten years, becoming GM's Global Lead in driver workload and driver vision.Over the last 20 years, Dr. Young's primary research interests have been in the effects of secondary tasks on driver attention and performance, particularly cell phone conversation. He was the first to suggest that cell phone conversation does not increase crash risk and has done much research and published many journal articles concerning that topic. He has also recently served as an expert witness in legal cases concerning cell phone conversation, driver distraction, and crash risk.Dr. Young began investigating autonomous vehicle safety. He found that many journal articles, company reports, and academic papers made safety claims (both positive and negative) about autonomous vehicles that contained systematic errors, biases, and best practice violations. This observation compelled him to write this book to improve the validity of future studies of autonomous vehicle safety.