Build a Machine Learning Model Hosting and Monitoring Dashboard

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.

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.

• Deploy Machine Learning Models with Flask
• Using FastAPI for ML Model Deployment
• Understanding Data and Concept Drift
• Building Monitoring Dashboards with Grafana
• Best Practices for MLOps Monitoring
• Open-Source Tools for ML Model Management