Loan Default Prediction in Retail Banking
Loan Default Prediction with MLOps in Retail Banking addresses a critical challenge for financial institutions: minimizing loan default risks while ensuring profitable lending. This project demonstrates how to develop a robust predictive model integrated with an MLOps pipeline for efficient deployment. By leveraging cloud-based services and best practices in machine learning, this solution automates the deployment and monitoring process, making it scalable, reliable, and maintainable. The project showcases the potential of combining MLOps with data science to enhance operational efficiency and decision-making in the financial sector.
MACHINE LEARNINGMLOPSMLFLOWDOCKERAWS
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Main Features
Predictive Modeling: Estimates the probability of personal loan defaults using advanced machine learning algorithms.
MLOps Pipeline: Implements a fully automated MLOps pipeline, ensuring seamless training, deployment, and monitoring of models.
Cloud Deployment: Deploys the best-performing model on AWS using Docker and Streamlit for scalability and reliability.
Experiment Tracking: Uses MLflow for model tracking, version control, and performance metrics visualization.
Technology Stack
Programming Language: Python
Dependency Management: Poetry
Machine Learning Frameworks: MLflow, Optuna (for hyperparameter tuning)
Visualization & Deployment: Streamlit for an interactive UI, Docker for containerization
Cloud Services: AWS (Elastic Container Registry and scalable hosting)
Version Control & CI/CD: Git, GitHub Actions