Quality assurance · Production
Calculating classification metrics in Power BI: Evaluating ML models in dashboards
The problem
Post-deployment ML model evaluation stays locked in Python notebooks, making it inaccessible to non-technical stakeholders such as product managers and support leads who need to monitor whether models are degrading.
Workflow diagram · grounded in source
1
Model ships to production
trigger
“once the model ships, evaluation does not stop”
2
Post-deployment monitoring
ai_action
“Monitoring models post-deployment is necessary for detecting data drift, concept drift, and triggering continual learning”
3
Notebook-locked evaluation
human_review
“Classification reports, confusion matrices, F1 scores — these are the instruments they use to decide whether a model is ready to ship”
4
Dashboard output for stakeholders
output
“From notebooks to dashboards: Evaluating ML models in Power BI”
Reported outcome
(not stated)
Reported stack
Power BIMLFlow
Source
https://medium.com/data-science-at-microsoft/calculating-classification-metrics-in-power-bi-4f9f3a2583df
Read source ↗Frequently asked questions
What did this team achieve with this AI workflow?
(not stated)
What tools did this team use?
Power BI, MLFlow.
How is this quality assurance AI workflow structured?
Model ships to production → Post-deployment monitoring → Notebook-locked evaluation → Dashboard output for stakeholders.