MLflow Projects - these are a standardized way to package up code related to a specific data science or machine learning "project". For example, if you have a workflow to pre-process data (step 1) and train a model (step 2), you could package this up into a an "MLproject" spec file similar to this. Many organizations use this format to build conformity across disparate teams working on projects, and to ensure projects are repeatable (e.g. model training happens the same way every time) across an entire code base.

MLflow Models - these are the easiest way to abstract the way in which a model was trained from the way it is deployed. For example, you may want to use the latest-and-greatest ML framework (say, Pytorch) but you're not sure how this model will be deployed: batch scoring using Spark? real time API endpoint? Using MLflow Models, when you train a model, it automatically generates multiple "flavors" of how that particular trained model can be deployed. Then you can deploy that however you like (e.g. as a Python function, aka. `pyfunc`) without worrying about the underlying ML framework.