quality_assurance · workflow
Calculating classification metrics in Power BI: Evaluating ML models in dashboards
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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Model ships to production
Once the model ships, evaluation does not stop.
Tools used
Power BIMLFlow
Outcome
(not stated)
Grounding & classification
Source type: technical build writeup
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anomaly detectiontools describedworkflow describedtechnical build writeupquality assurancemonitor detect alert