ML example with logging experiments and artifacts in remote MLflow server. Noting MLflow client (in Python) and server need to share the same (NFS) filesystem for artifacts storing.
# Custom Python Environment
python -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install pandas sklearn jupyter mlflow
# MLflow
mlflow server --backend-store-uri sqlite:///database.sqlite --default-artifact-root $(pwd)/mlruns &
# Simulate remote MLflow
export MLFLOW_TRACKING_URI=http://127.0.0.1:5000
# Jupyter
jupyter notebook &