Vehicle Insurance Data Pipeline

PythonFastAPIMLflowDVCGrafanaPrometheusMLOpsData PipelinesAWSCI/CD
Vehicle Insurance Data Pipeline
  • End-to-end ML pipeline for vehicle insurance risk prediction, featuring automated data orchestration, monitoring, and scalable API deployment.
  • This project implements a robust, end-to-end machine learning pipeline for vehicle insurance risk prediction.
  • Key features include automated data ingestion, preprocessing, model training, evaluation, and deployment using FastAPI microservices. DVC is used for version control, MLflow for experiment tracking, and Grafana with Prometheus for real-time monitoring and visualization.
  • The pipeline is optimized for scalability, reproducibility, and production readiness. It integrates cloud-native storage and deployment using AWS S3, EC2, and ECR, with CI/CD automation through GitHub Actions. Users interact with predictions via a web UI built with Jinja2 templates.