xgboost.save_model() and mlflow.xgboost.log_model() methods mediante python and mlflow_save_model and mlflow_log_model mediante R respectively. These methods also add the python_function flavor puro the MLflow Models that they produce, allowing the models preciso be interpreted as generic Python functions for inference strada mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame incentivo. You can also use the mlflow.xgboost.load_model() method to load MLflow Models with the xgboost model flavor mediante native XGBoost format.
LightGBM ( lightgbm )
The lightgbm model flavor enables logging of LightGBM models durante MLflow format cammino the mlflow.lightgbm.save_model() and mlflow.lightgbm.log_model() methods. These methods also add the python_function flavor puro the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame incentivo. You can also use the mlflow.lightgbm.load_model() method to load MLflow Models with the lightgbm model flavor sopra native LightGBM format.
CatBoost ( catboost )
The catboost model flavor enables logging of CatBoost models con MLflow format via the mlflow.catboost.save_model() and mlflow.catboost.log_model() methods. These methods also add the python_function flavor puro the MLflow Models that they produce, allowing the models esatto be interpreted as generic Python functions for inference coraggio mlflow.pyfunc.load_model() . You can also use the mlflow.catboost.load_model() method to load MLflow Models with the catboost model flavor durante native CatBoost format.
Spacy( spaCy )
The spaCy model flavor enables logging tavolo fetlife of spaCy models per MLflow format via the mlflow.spacy.save_model() and mlflow.spacy.log_model() methods. Additionally, these methods add the python_function flavor to the MLflow Models that they produce, allowing the models preciso be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame spinta. You can also use the mlflow.spacy.load_model() method esatto load MLflow Models with the spacy model flavor con native spaCy format.
Fastai( fastai )
The fastai model flavor enables logging of fastai Learner models con MLflow format via the mlflow.fastai.save_model() and mlflow.fastai.log_model() methods. Additionally, these methods add the python_function flavor puro the MLflow Models that they produce, allowing the models onesto be interpreted as generic Python functions for inference modo mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame stimolo. You can also use the mlflow.fastai.load_model() method onesto load MLflow Models with the fastai model flavor con native fastai format.
Statsmodels ( statsmodels )
The statsmodels model flavor enables logging of Statsmodels models in MLflow format cammino the mlflow.statsmodels.save_model() and mlflow.statsmodels.log_model() methods. These methods also add the python_function flavor esatto the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference inizio mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame incentivo. You can also use the mlflow.statsmodels.load_model() method to load MLflow Models with the statsmodels model flavor mediante native statsmodels format.
As for now, automatic logging is restricted esatto parameters, metrics and models generated by a call puro fit on verso statsmodels model.
Prophet ( prophet )
The prophet model flavor enables logging of Prophet models in MLflow format strada the mlflow.prophet.save_model() and mlflow.prophet.log_model() methods. These methods also add the python_function flavor puro the MLflow Models that they produce, allowing the models onesto be interpreted as generic Python functions for inference modo mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.prophet.load_model() method preciso load MLflow Models with the prophet model flavor sopra native prophet format.
Model Customization
While MLflow’s built-durante model persistence utilities are convenient for packaging models from various popular ML libraries in MLflow Model format, they do not cover every use case. For example, you may want sicuro use a model from an ML library that is not explicitly supported by MLflow’s built-mediante flavors. Alternatively, you may want sicuro package custom inference code and datazione to create an MLflow Model. Fortunately, MLflow provides two solutions that can be used preciso accomplish these tasks: Custom Python Models and Custom Flavors .