Understanding models in MLflow
On the MLflow platform, you have two main components available to manage models:
- Models: This module manages the format, library, and standards enforcement module on the platform. It supports a variety of the most used machine learning models: sklearn, XGBoost, TensorFlow, H20, fastai, and others. It has features to manage output and input schemas of models and to facilitate deployment.
- Model Registry: This module handles a model life cycle, from registering and tagging model metadata so it can be retrieved by relevant systems. It supports models in different states, for instance, live development, testing, and production.
An MLflow model is at its core a packaging format for models. The main goal of MLflow model packaging is to decouple the model type from the environment that executes the model. A good analogy of an MLflow model is that it’s a bit like a Dockerfile for a model, where you describe metadata of the model, and...
