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