DOCKER_STATUS.md

🐳 DAFU Docker Implementation Status

Current Status: ⚠️ Infrastructure Prepared - Services Not Active

Last Updated: October 8, 2025

What's Working ✅

ML Models (via Python)

• ✅ Isolation Forest fraud detection
• ✅ LSTM/GRU sequence models
• ✅ Model training and prediction
• ✅ Stream and batch processing
• ✅ Model persistence (save/load)
• ✅ Data visualization
• ✅ Result export

How to Use:

cd fraud_detection
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
cd src/models
python main.py

What's Prepared 📋

Docker Infrastructure

• ✅ docker-compose.yml (services commented out)
• ✅ Dockerfile (multi-stage build)
• ✅ Database schema (init-db.sql)
• ✅ Prometheus configuration
• ✅ Grafana datasources
• ✅ .dockerignore
• ✅ .env configuration

Documentation

• ✅ DOCKER_SETUP.md
• ✅ QUICK_START.md
• ✅ DOCKER_README.md
• ✅ DOCKER_IMPLEMENTATION_SUMMARY.md
• ✅ README.md (updated)

Helper Tools

• ✅ start.sh script
• ✅ Makefile

What's Pending 🔄

Critical Integration Tasks

1. API-ML Integration - Connect ML models to FastAPI endpoints
2. Database Connection - Implement ORM (SQLAlchemy)
3. Redis Integration - Implement caching layer
4. Celery Tasks - Implement background job processing

Why Services Are Commented Out

All services in docker-compose.yml are commented out because:

• FastAPI endpoints return mock data (no ML integration)
• No database connection implemented
• No Redis usage implemented
• No Celery tasks defined
• ML models work standalone via Python

Next Steps 📝

Phase 1: API-ML Integration (Priority)

• Load ML models in FastAPI startup
• Implement fraud scoring endpoint with real ML
• Implement batch processing endpoint
• Test API with ML models

Phase 2: Database Integration

• Set up SQLAlchemy ORM
• Connect to PostgreSQL
• Implement data persistence
• Test database operations

Phase 3: Service Activation

• Uncomment services in docker-compose.yml
• Test service integration
• Update documentation

How to Contribute

1. Work on Integration: Help integrate ML models with FastAPI
2. Test Features: Test ML models and report issues
3. Improve Docs: Enhance documentation

Questions?

• ML Models: Fully functional via Python
• Docker Services: Infrastructure ready, integration pending
• Timeline: Dependent on integration completion

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For current ML usage, see QUICK_START.md
For Docker infrastructure details, see DOCKER_SETUP.md