Assessing your Machine Learning Workloads

From the creator

In this episode of the #AzureEnablementShow, David Blank-Edelman is joined by Jason DeBoever, senior product manager, and Sudha Kumar, principal cloud solution architect to discuss how machine learning-specific assessments can help you avoid costly pitfalls like increased spend, loss of trust, and costly breaches using the five pillars of the Azure Well-Architected Framework. Resources • Azure Well-Architected Review https://aka.ms/azenable/78/01 • Azure Well-Architected Framework https://aka.ms/wellarchitected/framework • What is Azure Machine Learning? https://aka.ms/azenable/78/02 Related episodes • Architect successful workloads on Azure https://www.youtube.com/watch?v=UpQHmWxkVEU • Assess cloud readiness using Microsoft Assessments https://www.youtube.com/watch?v=1Hq1oyHXbvE To watch more episodes in the Well-Architected Series, check out our playlist: https://aka.ms/azenable/yt/wa-playlist Explore more cloud enablement resources! https://www.azure.com/enablement 0:00 Introduction 1:15 How we make sure we're doing this right? 2:57 How is a workload-specific assessment different than a general assessment? 4:34 What are some of the machine learning-specific questions I can expect? 7:11 What sort of recommendations can I expect from the assessment? 9:35 How will the assessments evolve over time? 11:13 What are the next steps? #Azure #AzureEnablementShow #WellArchitected

Published 2022-03-07 · Watch on YouTube ↗
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