quality_assurance · workflow
Monitoring ML models in production with FastAPI and Evidently AI
ML models in production produce wrong predictions, unexpected label distributions, and surprising inputs, but most practitioners treat deployment as the final step rather than the beginning of an ongoing monitoring responsibility.
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 · Prediction request received
An ML model endpoint receives POST requests and returns predictions.
Tools used
FastAPIEvidentlynannyMLpandasjoblib
Outcome
The described setup adds production visibility via a Data Drift dashboard that highlights distributional divergence between training data and live predictions, with prediction logging done as a non-blocking background task.
Grounding & classification
Source type: technical build writeup
11 fields verified against source quotes, 1 dropped as unverifiable.
anomaly detectiontools describedworkflow describedtechnical build writeupquality assurancemonitor detect alert