MachineInsight Pro — Predictive Maintenance
Telemetry in, failure-risk out — an IBM Watson AutoAI LightGBM model served through a Vercel serverless proxy.
Impact
99.4%
F1-weighted accuracy
Milliseconds
Telemetry to prediction
Live
Deployed on Vercel
Technology
Key decisions
- 01
A serverless proxy to hide the model key
Calling Watson directly from the browser would leak the API key and break on CORS. A Vercel function injects the credential server-side, so the secret never reaches the client.
- 02
AutoAI to find the model
Watson AutoAI ranked multiple pipelines and selected a LightGBM classifier with engineered features and tuned hyperparameters, instead of me hand-picking a model.
- 03
A telemetry-driven, real-time UI
The UI takes the five telemetry signals and shows the failure-risk prediction immediately with gauges and charts, so an operator reads it at a glance.
Architecture
- 01
Telemetry input
Torque, speed, temperature, tool wear, quality type
- 02
Vercel serverless /api/predict
Injects IBM credentials server-side, forwards the request
- 03
IBM Watson AutoAI
LightGBM classifier, automated feature engineering + HPO
- LightGBM
- 04
Prediction
Failure-risk returned in milliseconds
- 05
Dashboard
Glass-morphism UI with gauges and animations
- Chart.js
- GSAP
Scroll the diagram horizontally to explore →
The problem
Industrial maintenance is usually reactive — you fix a machine after it fails, and the downtime is expensive. Predicting failure from live telemetry needs a trained model, but calling it straight from the browser would expose the model's credentials and hit CORS limits.
How it works
I used IBM Watson AutoAI to generate, rank, and tune models on machine telemetry; it selected a LightGBM classifier with automated feature engineering and hyperparameter optimisation, reaching 99.4% F1-weighted accuracy. The browser sends five telemetry signals (torque, speed, temperature, tool wear, quality type) to a Vercel serverless function at /api/predict, which injects the IBM credentials server-side and forwards the request to the Watson AutoAI endpoint. The result comes back to a glass-morphism UI with Chart.js gauges and GSAP animations.