Deploying MLflow Open-Source Machine Learning Experiment Tracking on Ubuntu 24.04

MLflow is an open-source platform for managing the machine learning lifecycle — experiment tracking, model registry, and reproducible runs. This guide deploys MLflow using Docker Compose with a PostgreSQL backend, S3-compatible artifact storage, basic-auth, and Traefik handling automatic HTTPS, then logs a sample scikit-learn run. By the end, you'll have MLflow recording experiments at your domain over HTTPS. Prerequisite: An S3-compatible bucket (e.g. Vultr Object Storage) with access key, secret key, region, and endpoint URL. Set Up the Directory Structure 1. Create the project directory: $ mkdir -p ~/mlflow $ cd ~/mlflow 2. Create the environment file: $ nano .env DOMAIN = mlflow.example.com LETSENCRYPT_EMAIL = admin@example.com POSTGRES_USER = mlflow POSTGRES_PASSWORD = StrongDatabasePassword123 MLFLOW_AUTH_CONFIG_PATH = /app/basic_auth.ini MLFLOW_FLASK_SERVER_SECRET_KEY = GENERATED_SECRET_KEY S3_BUCKET = mlflow-artifacts S3_ACCESS_KEY = YOUR_ACCESS_KEY S3_SECRET_KEY = YOUR_SECRET_KEY S3_REGION = YOUR_REGION S3_ENDPOINT = https://YOUR_OBJECT_STORAGE_ENDPOINT 3. Create the basic-auth configuration: $ nano basic_auth.ini [mlflow] default_permission = READ database_uri = sqlite:///basic_auth.db admin_username = admin admin_password = ADMIN_PASSWORD authorization_function = mlflow.server.auth:authenticate_request_basic_auth 4. Create a Dockerfile that adds the auth-server extras and Postgres/S3 clients to the official image: $ nano Dockerfile FROM ghcr.io/mlflow/mlflow:v3.10.1 RUN pip install --no-cache-dir psycopg2-binary boto3 'mlflow[auth]' Deploy with Docker Compose 1. Create the Compose manifest: $ nano docker-compose.yml services : traefik : image : traefik:v3.6 container_name : traefik command : - " --providers.docker=true" - " --providers.docker.exposedbydefault=false" - " --entrypoints.web.address=:80" - " --entrypoints.websecure.address=:443" - " --entrypoints.web.http.redirections.entrypoint.to=websecure" - " --entrypoints.web.http.redirections.entrypoint