All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- Initial release of ML Service Framework
0.1.0 - 2026-02-01
- Core ML pipeline with training and inference
- Data layer with PostgreSQL, MongoDB, S3, and Azure Blob connectors
- Multiple ML models: Random Forest, XGBoost
- Data preprocessing pipeline with feature engineering
- Cross-validation and model evaluation
- Hyperparameter tuning (Grid Search, Random Search, Optuna)
- MLflow experiment tracking integration
- Model registry for version management
- FastAPI REST API for model serving
- Prometheus metrics and monitoring
- Data drift detection using Evidently
- Docker and docker-compose support
- Comprehensive test suite with pytest
- CI/CD with GitHub Actions
- Pre-commit hooks for code quality
- Pydantic-based configuration management
- CLI tool for creating new projects (
ml-create-project) - Complete documentation and examples
- Added security scanning in CI/CD pipeline
- Input validation with Pydantic