Exploring model flavors in MLflow
Model flavors in MLflow are basically the different models of different libraries supported by MLflow. This functionality allows MLflow to handle the model types with native libraries of each specific model and support some of the native functionalities of the models. The following list presents a selection of representative models to describe and illustrate the support available in MLflow:
mlflow.tensorflow: TensorFlow is by far one of the most used libraries, particularly geared toward deep learning. MLflow integrates natively with the model format and the monitoring abilities by saving logs in TensorBoard formats. Auto-logging is supported in MLflow for TensorFlow models. The Keras model in Figure 5.5 is a good example of TensorFlow support in MLflow.mlflow.h2o: H2O is a complete machine learning platform geared toward the automation of models and with some overlapping features with MLflow. MLflow provides the ability to load (load_model...
