All work
2026MLOps & backend engineer

FloodGuard AI

Urban flood early-warning platform with a fully automated MLOps lifecycle.

View Source Code (GitHub)
FloodGuard AI operations console showing Mumbai metro flood risk: active alert counters, 24h rainfall, model confidence, a live Leaflet risk map with severity-coded zones, and a sorted active-alerts feed.
FloodGuard AI — ops console with live risk map, alert feed and model-confidence monitoring.

TL;DR

An urban flood early-warning system where the model is treated as a production asset: versioned data, tracked experiments, containerized serving, and continuous drift monitoring.

Inference Latency
~42ms p50 (PyTorch CPU / Batch=1)
Drift Alerting
Evidently AI (PSI > 0.25)

Problem

Flood prediction models rot silently. Without data versioning, experiment tracking, and drift monitoring, a model that scored well once quietly degrades before anyone notices.

Approach

  1. 01

    Versioned datasets with DVC so every model artifact traces back to the exact data that produced it.

  2. 02

    Tracked experiments and metrics in MLflow to compare runs objectively instead of by intuition.

  3. 03

    Served predictions through a REST API with a heuristic fallback so the endpoint never hard-fails.

  4. 04

    Containerized with Docker and automated build, test, and deploy through GitHub Actions.

  5. 05

    Monitored production inputs with Evidently AI to catch distribution drift early.

Key architectural decision

Architected a pluggable ML pipeline separating model serving from backend core logic using FastAPI, allowing seamless algorithm upgrades without disrupting production endpoints.

System Architecture & Data Pipeline

FloodGuard AI end-to-end system architecture showing users, dashboard, FastAPI services, prediction layer, MongoDB persistence, monitoring, operational response, and MLOps delivery flow.
End-to-end telemetry ingestion, inference pipeline, experiment tracking, and drift monitoring flow.

Impact

  • Model updates ship through an automated pipeline instead of manual redeploys.
  • Every prediction path has a fallback, keeping the warning service available under model failure.
  • Drift reports surface degradation before forecast quality visibly drops.

What shipped

DVC + MLflow pipeline

Reproducible data and experiment lineage per run.

Dockerized serving

REST inference container with graceful fallback.

Drift dashboard

Evidently AI reports wired into the CI schedule.

Stack

  • Python
  • AWS EC2
  • Docker Compose
  • Nginx
  • FastAPI
  • React
  • DVC
  • MLflow
  • Docker
  • GitHub Actions
  • Evidently AI