docker-compose.yml

version: "3.9"

services:
  # ── MLflow tracking server ────────────────────────────────────────────────
  mlflow:
    image: python:3.9-slim
    container_name: mlflow-server
    working_dir: /mlflow
    volumes:
      - mlflow_data:/mlflow
    ports:
      - "5000:5000"
    command: >
      bash -c "pip install mlflow boto3 --quiet &&
               mlflow server
                 --backend-store-uri sqlite:///mlflow.db
                 --default-artifact-root /mlflow/artifacts
                 --host 0.0.0.0
                 --port 5000"
    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:5000/health')"]
      interval: 15s
      timeout: 5s
      retries: 5

  # ── Churn API ─────────────────────────────────────────────────────────────
  api:
    build: .
    container_name: churn-api
    ports:
      - "8000:8000"
    environment:
      - MLFLOW_TRACKING_URI=http://mlflow:5000
      - MLFLOW_EXPERIMENT_NAME=churn-prediction
      - MODEL_NAME=telco_linear
      - LOG_LEVEL=INFO
    depends_on:
      mlflow:
        condition: service_healthy
    volumes:
      - ./models:/app/models:ro   # mount local model artifacts read-only

volumes:
  mlflow_data: