title: Differencies between MLflow deployment possibilities
body: <p>Can someone please explain what are the main use cases when deciding how to serve a model from MLflow:</p>
  <ul>
  <li>using command line &quot;mlflow models serve -m ....&quot;</li>
  <li>deploying local Docker container with the same model</li>
  <li>deploying model online for example on AWS Sagemaker</li>
  </ul>
  <p>I am mainly interested in differencies between option A and B because as I understand both can be accessed as REST API endpoints. And I assume if network rules are in place then both can be called also externally.</p>