[ospillinger]

From the MLflow Models docs: "An MLflow Model is a standard format for packaging machine learning models that can be used in a variety of downstream tools—for example, real-time serving through a REST API or batch inference on Apache Spark. The format defines a convention that lets you save a model in different “flavors” that can be understood by different downstream tools."

Cortex is what they are referring to as a downstream tool for real-time serving through a REST API. In other words, MLflow helps with model management and packaging, whereas Cortex is a platform for running real-time inference at scale. We are working on supporting more model packaging formats and I think it's a good idea to support the MLflow format as well.