microsoft/Application-Insights-Workbooks
Sep 2025 – Sep 2025
1 Month active
Languages Used
KQL
Technical Skills
Azure Machine LearningData AnalysisKusto Query LanguageMonitoringObservability
Vinutha Karanth
PROFILE
Vinutha Karanth
Worked on the microsoft/Application-Insights-Workbooks repository to deliver KQL-driven improvements for the Azure ML Studio Foundry Dashboard, focusing on metrics and token usage. Consolidated and refined Kusto Query Language logic to enhance the accuracy and clarity of key metrics such as total tokens, average inference duration, total inference calls, and error rates. Improved AI quality and risk scoring calculations by implementing better deduplication and filtering techniques. Updated aggregation and percentage-change calculations for token usage, resulting in a more reliable and actionable monitoring dashboard. Leveraged skills in Azure Machine Learning, data analysis, and observability to support data-driven decision-making.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
26
Activity Months1
Your Network
4971 peopleSame Organization
@microsoft.com4939
GitOpsMember
Ananta GuptaMember
Abi GicicMember
Abigail HartmanMember
Abram SandersonMember
Adam EttenbergerMember
Adam KrantzMember
Alexandre GattikerMember
Ami HollanderMember
Work History
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 summary for microsoft/Application-Insights-Workbooks: Delivered Azure ML Studio Foundry Dashboard: KQL Improvements for Metrics and Token Usage. Consolidated KQL logic across the Foundry Dashboard and workbook to improve data accuracy and presentation of key metrics including total tokens, average inference duration, total inference calls, and error rates. Refined AI quality and risk/safety score calculations via improved deduplication and filtering. Improved token usage aggregation and percentage-change calculations for a clearer token usage dashboard, enabling more reliable monitoring and data-driven decisions. Commit references: 1193395368629551de8a03eb28ef1f0a2dac62b8 (add (#3079)) and b3f3c1999a0df659cf21c082278e6c2e8e4867f0 (Update total_token query and token usage chart query (#3086)).
2 Commits • 1 Features
Sep 1, 2025September 2025 summary for microsoft/Application-Insights-Workbooks: Delivered Azure ML Studio Foundry Dashboard: KQL Improvements for Metrics and Token Usage. Consolidated KQL logic across the Foundry Dashboard and workbook to improve data accuracy and presentation of key metrics including total tokens, average inference duration, total inference calls, and error rates. Refined AI quality and risk/safety score calculations via improved deduplication and filtering. Improved token usage aggregation and percentage-change calculations for a clearer token usage dashboard, enabling more reliable monitoring and data-driven decisions. Commit references: 1193395368629551de8a03eb28ef1f0a2dac62b8 (add (#3079)) and b3f3c1999a0df659cf21c082278e6c2e8e4867f0 (Update total_token query and token usage chart query (#3086)).
September 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
KQL
Technical Skills
Azure Machine LearningData AnalysisKusto Query LanguageMonitoringObservability
Repositories Contributed To
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