MLOps Pipeline Automation - Mlops Project

Project Overview

Designed and implemented a comprehensive MLOps pipeline that automates the entire machine learning lifecycle from data ingestion to model deployment and monitoring. This system eliminates manual intervention in ML workflows while ensuring reproducibility and scalability.

Challenge

Manual ML model deployment processes were error-prone, time-consuming, and lacked proper versioning, making it difficult to maintain model performance in production environments.

Solution

Built automated MLOps pipeline with MLflow for experiment tracking, GitHub Actions for CI/CD, Docker for containerization, and Kubernetes for orchestration with automatic model retraining capabilities.

Results & Impact

• Reduced model deployment time from hours to 15 minutes
• Implemented automatic model retraining with performance monitoring
• Achieved 99.5% pipeline reliability with proper error handling
• Enabled rapid experimentation with version-controlled model artifacts

Technologies Used