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