Which of the following describes the concept of MLflow Model flavors?

MLflow Model flavors are a key concept that makes MLflow Models powerful: they are a convention that deployment tools can use to understand the model, which makes it possible to write tools that work with models from any ML library without having to integrate each tool with each library. MLflow defines a set of built-in model flavors for popular ML frameworks, such as scikit-learn, TensorFlow, PyTorch, etc. Each flavor defines a standard format for model data and a common interface for model inference. For example, the python_function flavor allows any model to be loaded as a Pythonfunction that can be applied to input data. MLflow also supports custom model flavors for user-defined models or community-developed flavors. MLflow Model flavors enable model portability and interoperability across different platforms and environments12 References:

MLflow Models — MLflow 2.9.1 documentation

Models, Flavors, and PyFuncs in MLflow