Docker compose file to deploy MLflow tracking server

docker-compose.yml

version: '3.3'

services:
    db:
        restart: always
        image: mysql/mysql-server:5.7.28
        container_name: mlflow_db
        expose:
            - "3306"
        networks:
            - backend
        environment:
            - MYSQL_DATABASE=${MYSQL_DATABASE}
            - MYSQL_USER=${MYSQL_USER}
            - MYSQL_PASSWORD=${MYSQL_PASSWORD}
            - MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD}
        volumes:
            - dbdata:/var/lib/mysql

    web:
        restart: always
        build: ./mlflow
        image: mlflow_server
        container_name: mlflow_server
        expose:
            - "5000"
        networks:
            - frontend
            - backend
        environment:
            - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
            - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
            - AWS_DEFAULT_REGION=${AWS_DEFAULT_REGION}
        command: mlflow server --backend-store-uri mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@db:3306/${MYSQL_DATABASE} --default-artifact-root s3://mlflow_bucket/mlflow/ --host 0.0.0.0        

    nginx:
        restart: always
        build: ./nginx
        image: mlflow_nginx
        container_name: mlflow_nginx
        ports:
            - "80:80"
        networks:
            - frontend
        depends_on:
            - web

networks:
    frontend:
        driver: bridge
    backend:
        driver: bridge

volumes:
    dbdata:

why using gist and not a repo? to better understand the tree structure you have for the MLflow???