The xgboost model flavor enables logging of XGBoost models con MLflow format inizio the mlflow

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 esatto the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference modo mlflow.pyfunc.load_model() . This loaded quiz datemyage PyFunc model can only be scored with DataFrame molla. You can also use the mlflow.xgboost.load_model() method onesto load MLflow Models with the xgboost model flavor con native XGBoost format.

LightGBM ( lightgbm )

The lightgbm model flavor enables logging of LightGBM models mediante MLflow format cammino the mlflow.lightgbm.save_model() and mlflow.lightgbm.log_model() methods. These methods also add the python_function flavor preciso the MLflow Models that they produce, allowing the models esatto 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.lightgbm.load_model() method sicuro load MLflow Models with the lightgbm model flavor durante native LightGBM format.

CatBoost ( catboost )

The catboost model flavor enables logging of CatBoost models con MLflow format inizio the mlflow.catboost.save_model() and mlflow.catboost.log_model() methods. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models to be interpreted as generic Python functions for inference modo mlflow.pyfunc.load_model() . You can also use the mlflow.catboost.load_model() method onesto load MLflow Models with the catboost model flavor in native CatBoost format.

Spacy( spaCy )

The spaCy model flavor enables logging of spaCy models sopra MLflow format strada the mlflow.spacy.save_model() and mlflow.spacy.log_model() methods. Additionally, these methods add the python_function flavor esatto the MLflow Models that they produce, allowing the models sicuro be interpreted as generic Python functions for inference cammino mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.spacy.load_model() method onesto load MLflow Models with the spacy model flavor durante native spaCy format.

Fastai( fastai )

The fastai model flavor enables logging of fastai Learner models durante MLflow format strada the mlflow.fastai.save_model() and mlflow.fastai.log_model() methods. Additionally, these methods add the python_function flavor to the MLflow Models that they produce, allowing the models esatto be interpreted as generic Python functions for inference coraggio mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame incentivo. You can also use the mlflow.fastai.load_model() method sicuro load MLflow Models with the fastai model flavor mediante native fastai format.

Statsmodels ( statsmodels )

The statsmodels model flavor enables logging of Statsmodels models per MLflow format cammino the mlflow.statsmodels.save_model() and mlflow.statsmodels.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 modo mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.statsmodels.load_model() method preciso load MLflow Models with the statsmodels model flavor in native statsmodels format.

As for now, automatic logging is restricted sicuro parameters, metrics and models generated by verso call to fit on verso statsmodels model.

Prophet ( prophet )

The prophet model flavor enables logging of Prophet models mediante MLflow format modo the mlflow.prophet.save_model() and mlflow.prophet.log_model() methods. These methods also add the python_function flavor preciso 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 spinta. You can also use the mlflow.prophet.load_model() method onesto load MLflow Models with the prophet model flavor in native prophet format.

Model Customization

While MLflow’s built-sopra 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 esatto use verso model from an ML library that is not explicitly supported by MLflow’s built-durante flavors. Alternatively, you may want puro package custom inference code and momento preciso create an MLflow Model. Fortunately, MLflow provides two solutions that can be used sicuro accomplish these tasks: Custom Python Models and Custom Flavors .