feat: add ML service scaffolding with Python FastAPI, Docker, and MLflow setup

docker-compose.yml

@@ -5,9 +5,74 @@ services:
       - "3000:3000"
     volumes:
       - candle-data:/app/data
+      - ml-data:/app/ml-data
     environment:
       - NODE_ENV=production
+      - INFERENCE_API_URL=http://ml-service:8001
+      - INFERENCE_API_TIMEOUT=30000
+      - INFERENCE_BATCH_TIMEOUT=120000
+      - NEXT_PUBLIC_PREDICTIONS_ENABLED=true
     restart: unless-stopped
+    depends_on:
+      - ml-service
+  
+  ml-service:
+    build: ./services/ml
+    ports:
+      - "8001:8001"
+    volumes:
+      - ml-data:/app/data
+      - mlflow-data:/app/mlruns
+    environment:
+      - MLFLOW_TRACKING_URI=http://mlflow:5000
+      - DATABASE_URL=postgresql://ml_user:ml_password@postgres:5432/ml_db
+      - PYTHONUNBUFFERED=1
+    restart: unless-stopped
+    depends_on:
+      postgres:
+        condition: service_healthy
+      mlflow:
+        condition: service_started
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8001/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 40s
+  
+  mlflow:
+    image: ghcr.io/mlflow/mlflow:v2.10.0
+    ports:
+      - "5000:5000"
+    volumes:
+      - mlflow-data:/mlflow
+    command: >
+      mlflow server
+      --backend-store-uri /mlflow
+      --default-artifact-root /mlflow/artifacts
+      --host 0.0.0.0
+      --port 5000
+    restart: unless-stopped
+  
+  postgres:
+    image: postgres:16-alpine
+    ports:
+      - "5432:5432"
+    volumes:
+      - postgres-data:/var/lib/postgresql/data
+    environment:
+      - POSTGRES_USER=ml_user
+      - POSTGRES_PASSWORD=ml_password
+      - POSTGRES_DB=ml_db
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U ml_user -d ml_db"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
 
 volumes:
   candle-data:
+  ml-data:
+  mlflow-data:
+  postgres-data: