Deployed experiment / model serving
Live ML Demo
Manufacturing Defect Prediction — an inspectable Random Forest classifier served as a FastAPI microservice.
Evaluation snapshot
Held-out test-set metrics from this synthetic-data experiment.
Test accuracy
0.881
Train Samples
4000
Test Samples
1000
Algorithm
Random Forest
Feature Importances
Make a Prediction
Enter manufacturing sensor values to get a real-time defect prediction.
Prediction Result
Interpretation
What this result can—and cannot—tell us
Useful here
The interface exposes model inputs, response probabilities, and feature importances so the prediction path is visible rather than treated as a black box.
Not a deployment claim
Synthetic data cannot establish field performance. A real deployment would require representative labels, calibration, threshold selection, drift monitoring, and human review.
Technical Details
Model
Random Forest Classifier, scikit-learn, trained on 5,000 synthetic manufacturing sensor samples.
Serving
FastAPI + Uvicorn, Docker container, GitHub Actions CI/CD, deployed on Azure App Service.
Endpoint
POST /predict
Content-Type: multipart/form-data