Summary

Comprehensive refactoring of the LeanAI body fat prediction project from a monolithic structure to a production-ready MLOps pipeline. The codebase has been reorganized with modular components, proper testing infrastructure, and containerization support while maintaining the core SVR model achieving <1% error (MAE: 0.10, R²: 0.9996).

Key Changes

Core ML Pipeline

• Modularized training (modeling/train.py): Extracted feature engineering (polynomial features, RFE, PCA) into reusable functions with explicit model building and evaluation
• Prediction module (modeling/predict.py): Separated inference logic with support for single predictions and batch processing
• Dataset processing (dataset.py): Implemented feature engineering pipeline (BMI, waist-to-hip ratios, arm ratios) with outlier removal using z-score normalization

API & Deployment

• Refactored FastAPI (api/main.py): Simplified endpoint structure with proper CORS configuration, health checks, and environment-based model loading
• MLOps FastAPI wrapper (mlops/src/fastapi_app/main.py): Added MLflow model registry integration alongside local model loading
• Docker improvements: Updated all Dockerfiles to Python 3.12, added health checks, and improved layer caching

Testing & Quality

• Comprehensive test suite (tests/): Added pytest tests for API endpoints, dataset processing, and model training
• CI/CD pipeline (.github/workflows/ci.yml): GitHub Actions workflow for linting, testing, and Docker builds
• Code quality: Integrated Ruff for linting/formatting with proper configuration in pyproject.toml

MLOps Infrastructure

• Drift detection (mlops/src/monitoring/drift_detection.py): Evidently AI integration for data, target, and regression performance monitoring
• Retraining pipeline (mlops/src/retraining/retrain_flow.py): Metaflow-based automated retraining triggered by drift detection
• MLflow integration (mlops/src/train_and_log_mlflow.py): Model tracking and experiment logging

Documentation & Configuration

• Streamlined README: Condensed from 301 to 241 lines with quick-start guides, tech stack overview, and project structure
• Updated dependencies: Pinned versions in requirements.txt and pyproject.toml with optional dependency groups (mlops, dev, viz)
• Makefile modernization: Added targets for data processing, plotting, training, API, testing, and Docker operations
• Cross-platform support: Updated pixi.toml for Linux, macOS, and Windows with focused dependency set

Visualization

• EDA module (plots.py): Refactored plotting functions for distributions, correlation heatmaps, target relationships, and outlier detection

Notable Implementation Details

• Feature engineering pipeline uses PolynomialFeatures (degree=2) → RFE (8 features) → PCA (5 components) before SVR
• Model artifacts saved as pickle files with joblib for reproducibility
• API supports both JSON and HTML form-based predictions with proper content negotiation
• Docker Compose includes optional Jupyter service for development and training service for batch processing
• Comprehensive error handling and logging throughout the pipeline

https://claude.ai/code/session_018HH3y8TvMgXG3PEmdEjoRS