Assessing your Machine Learning Workloads

  Рет қаралды 1,217

Microsoft Developer

Microsoft Developer

Күн бұрын

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 aka.ms/azenable/78/01
• Azure Well-Architected Framework aka.ms/wellarchitected/framework
• What is Azure Machine Learning? aka.ms/azenable/78/02
Related episodes
• Architect successful workloads on Azure
• Architect successful w...
• Assess cloud readiness using Microsoft Assessments
• Assess cloud readiness...
To watch more episodes in the Well-Architected Series, check out our playlist: aka.ms/azenable/yt/wa-playlist
Explore more cloud enablement resources!
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

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