Unified ML Pipeline
Project Overview
Design and implementation of a unified ML pipeline for a banking institution, which accelerated model deployment and improved the stability of predictions.
The Challenge
The client had fragmented ML processes with different approaches across individual teams:
- Long deployment times for new models (averaging 10+ days)
- Inconsistent model quality
- Difficulty in tracking versions and experiments
The Solution
Architecture
- Databricks as the central platform for ML
- MLflow for tracking experiments and model registry
- Optuna for automated hyperparameter tuning
- Docker containers for a consistent environment
Key Features
- Automated feature engineering pipeline
- Centralized model registry with versioning
- A/B testing for gradual deployment
- Monitoring and alerting for model drift
The Results
| Metric | Before | After | Improvement |
|---|---|---|---|
| Model deployment time | 10 days | 2 days | -8 days |
| Model accuracy | baseline | +15% | +15% |
| Pipeline execution time | 4 hours | 2.4 hours | -40% |
Technology Stack
- Python, PySpark
- Databricks, MLflow
- Optuna, Docker
- GitHub Actions (CI/CD)