Car Price Prediction MLOps Pipeline
Implemented end-to-end MLOps pipeline for car price regression covering data preprocessing, model training with XGBoost, deployment, and continuous monitoring. System includes experiment tracking with MLflow, production-ready API with FastAPI, responsive web interface with Next.js, and real-time observability using Prometheus and Grafana.
Technologies Used
Project Overview
This project builds an end-to-end car price prediction system with MLOps approach, covering the entire machine learning lifecycle from data preprocessing, model training, deployment, monitoring, to continuous improvement. The system not only produces predictive models but also ensures models are production-ready, performance-monitored, and accessible through web interface and API.
System Architecture
Python & XGBoost: Building and training car price regression model with high prediction performance.
MLflow: Experiment tracking, model registry, and versioning for more controlled model deployment process.
FastAPI: Fast and scalable backend API with input validation and automatic documentation support.
Next.js: Responsive and interactive web frontend for prediction needs.
Prometheus & Grafana: Real-time API, model, and system resource performance monitoring.
Docker & Docker Compose: Ensuring environment consistency between development and production through containerization.
Key Features
End-to-End MLOps Pipeline: System covers the entire machine learning cycle from data preprocessing, model training, deployment, to continuous performance monitoring.
Responsive Web-Based Prediction Interface: Users can perform single and batch predictions through a responsive and easy-to-use web interface.
Experiment Tracking & Model Versioning: Every experiment, hyperparameter tuning, and model evaluation result is recorded and managed using MLflow.
Production-Ready API Layer: System provides scalable and well-documented prediction API to support integration with other systems.
Monitoring & Observability: System and model performance is monitored in real-time to ensure reliability and prediction quality is maintained.
Project Outcome
✓ Production-ready prediction system with full lifecycle management and monitoring