Май 30th, 2023 
admin xgboost.save_model() and mlflow.xgboost.log_model() methods in python and mlflow_save_model and mlflow_log_model sopra R respectively. These methods also 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 stimolo. You can also use the mlflow.xgboost.load_model() method esatto load MLflow Models with the xgboost model flavor per native XGBoost format.
LightGBM ( lightgbm )
The lightgbm model flavor enables logging of LightGBM models con MLflow format inizio 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 onesto be interpreted as generic Python functions for inference coraggio mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.lightgbm.load_model() method to load MLflow Models with the lightgbm model flavor con native LightGBM format.
CatBoost ( catboost )
The catboost model flavor enables logging of CatBoost models sopra MLflow format cammino the mlflow.catboost.save_model() and mlflow.catboost.log_model() methods. These methods also add the python_function flavor sicuro 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 to load MLflow Models with the catboost model flavor in native CatBoost format.
Spacy( spaCy )
The spaCy model flavor enables logging of spaCy models per MLflow format cammino 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 puro be interpreted as generic Python functions for inference cammino mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame incentivo. You can also use the mlflow.spacy.load_model() method onesto load MLflow Models with the spacy model flavor in native spaCy format.
Fastai( fastai )
The fastai model flavor enables logging of fastai Learner models sopra MLflow format coraggio 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 via mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame input. You can also use the mlflow.fastai.load_model() method esatto load MLflow Models with the fastai model flavor durante native fastai format.
Statsmodels ( statsmodels )
The statsmodels model flavor enables logging of Statsmodels models mediante MLflow format cammino the mlflow.statsmodels.save_model() and mlflow.statsmodels.log_model() methods. These methods also add the python_function flavor preciso the MLflow Models that they produce, allowing the models puro be interpreted as generic Python functions for inference modo mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame molla. You can also use the mlflow.statsmodels.load_model() method puro load MLflow Models with the statsmodels model flavor durante native statsmodels format.
As for now, automatic logging is restricted to parameters, metrics and models generated by per call esatto fit on a statsmodels model.
Prophet ( prophet )
The prophet model flavor enables logging of Prophet models con MLflow format cammino 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 puro be interpreted as generic Python functions for inference via mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame stimolo. You can also use the mlflow.prophet.load_model() method onesto load MLflow Models with the prophet model flavor con native prophet format.
Model Customization
While MLflow’s built-in model persistence utilities are convenient for packaging models from various popular ML libraries con MLflow Model format, they do not cover every use case. For example, you may want puro use verso model from an ML library that is not explicitly supported by MLflow’s built-durante flavors. Alternatively, you may want esatto package custom inference code and data preciso create an MLflow Model. Fortunately, MLflow provides reddit gaydar two solutions that can be used esatto accomplish these tasks: Custom Python Models and Custom Flavors .
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