Build a Machine Learning Model Hosting and Monitoring Dashboard
Create a platform where data scientists can upload, deploy, and monitor machine learning models easily, ensuring models stay healthy, accurate, and production-ready.Deploying ML models into production is only half the battle. Ensuring the models perform well over time, don't suffer from data drift, and stay reliable is critical. Without monitoring tools, ML systems risk degrading unnoticed.
Build a web platform where ML models can be uploaded, served as APIs (REST endpoints), and continuously monitored for key metrics such as prediction accuracy, request latency, model drift, and data distribution shifts — making MLOps manageable for teams and individuals.
Seamless Model Hosting
Deploy trained models (Pickle, ONNX, TensorFlow SavedModels) easily and expose them via REST APIs for use in applications.
Live Model Monitoring
Track metrics like response time, request volume, prediction confidence scores, and detect anomalies or drifts automatically.
Data Drift and Model Drift Detection
Monitor input feature distributions and output prediction changes over time to catch drift early and retrain models if needed.
Alerting and Dashboard Analytics
Set up email or webhook alerts for anomaly thresholds, visualize model performance, and manage multiple deployed versions.
Users upload trained ML models, which are deployed automatically as APIs on the server. Monitoring agents track incoming requests, prediction outputs, and input feature distributions. Dashboards display live metrics and drift analysis results for model health monitoring.
- Upload models via the dashboard (Pickle, H5, ONNX, SavedModel formats).
- Expose models automatically through generated API endpoints for inference.
- Track live metrics: prediction times, success rates, confidence distributions.
- Monitor data drift by comparing training data statistics with incoming data streams.
- Trigger drift alerts, recommend retraining, or rollback to previous model versions if needed.
Frontend Development
Next.js, React.js for model management UI, deployment dashboards, and monitoring charts
Backend Model Serving and Monitoring Engine
Flask/FastAPI for model serving APIs; Node.js (Express.js) for dashboard backend, drift analysis modules, alert triggers
Database and Storage
MongoDB/PostgreSQL for model metadata, request logs, drift reports, model versioning, and alert logs
Optional Enhancements
Prometheus + Grafana for advanced metric collection; AWS S3/Firebase for model artifact storage; Email alerts using SendGrid
1. Model Upload and Registration
Enable users to upload models with basic metadata (model type, input schema, training data stats) to the server.
2. API Deployment and Inference Service
Auto-wrap models into Flask/FastAPI endpoints that serve predictions and record inference logs for analysis.
3. Real-Time Metric Collection
Record metrics like inference latency, confidence scores, and success rates for every prediction request.
4. Drift Detection and Alerting
Continuously compare live input distributions with original training distributions to detect drift and trigger alerts.
5. Monitoring Dashboard and Reporting
Display key metrics on dashboards, show drift reports, manage deployed model versions, and allow rollback or retrain suggestions.
Ready to Manage ML Models Like a Pro?
Build your Machine Learning Model Hosting and Monitoring Dashboard — ensure your models stay accurate, reliable, and production-ready at all times!
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