Lilly Tech Systems
Build an AI Dashboard
Build a production-ready ML monitoring dashboard with Streamlit from scratch. This hands-on project walks you through connecting data sources, visualizing model performance, detecting data drift, tracking infrastructure costs, and adding interactive features — all with full working code.
Project Build Path
Follow these lessons in order to build the complete AI monitoring dashboard step by step, or jump to any section you need.
1. Project Setup
Architecture overview, Streamlit basics, data source planning, tech stack selection, and project scaffolding with full code.
2. Data Connectors
Connect to Prometheus, PostgreSQL, and S3 for ML metrics. Build reusable connector classes and a unified data layer.
3. Model Performance Views
Accuracy over time, confusion matrix heatmaps, feature importance bar charts, and precision-recall curves with Plotly.
4. Data Drift Monitoring
Distribution plots, PSI and KS test visualizations, drift alert thresholds, and automated notification triggers.
5. Cost & Infrastructure
GPU utilization gauges, API cost breakdowns, inference latency percentiles, and resource usage time series.
6. Interactive Features
Dynamic filters, date range pickers, model comparison views, CSV/PDF export, and drill-down capabilities.
7. Enhancements & Deployment
Auto-refresh, Streamlit Cloud deployment, authentication, performance optimization, and frequently asked questions.
What You Will Build
By the end of this project, you will have a fully functional ML monitoring dashboard that can:
Track Model Performance
Monitor accuracy, precision, recall, and F1 scores over time with interactive Plotly charts and confusion matrix heatmaps.
Detect Data Drift
Visualize feature distribution changes with PSI and KS test plots, and trigger alerts when drift exceeds thresholds.
Monitor Infrastructure Costs
Track GPU utilization, API call costs, and inference latency in real time with breakdowns by model and endpoint.
Interactive Exploration
Filter by date ranges, compare models side by side, drill into specific metrics, and export reports as CSV or PDF.