xgboost.save_model() and mlflow.xgboost.log_model() methods sopra python and mlflow_save_model and mlflow_log_model mediante R respectively. These methods also add the python_function flavor to 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.xgboost.load_model() method puro load MLflow Models with the xgboost model flavor sopra native XGBoost format.
LightGBM ( lightgbm )
The lightgbm model flavor enables logging of LightGBM models sopra MLflow format coraggio 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 sicuro 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 preciso load MLflow Models with the lightgbm model flavor sopra native LightGBM format.
CatBoost ( catboost )
The catboost model flavor enables logging of CatBoost models mediante MLflow format coraggio 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 puro be interpreted as generic Python functions for inference modo mlflow.pyfunc.load_model() . You can also use the mlflow.catboost.load_model() method sicuro load MLflow Models with the catboost model flavor con native CatBoost format.
Spacy( spaCy )
The spaCy model flavor enables logging of spaCy models per MLflow format inizio 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 onesto be interpreted profili imeetzu 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 in native spaCy format.
Fastai( fastai )
The fastai model flavor enables logging of fastai Learner models in MLflow format inizio the mlflow.fastai.save_model() and mlflow.fastai.log_model() methods. Additionally, these methods add the python_function flavor onesto 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 input. 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 in 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 puro 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.statsmodels.load_model() method preciso load MLflow Models with the statsmodels model flavor in native statsmodels format.
As for now, automatic logging is restricted preciso parameters, metrics and models generated by per call to fit on per statsmodels model.
Prophet ( prophet )
The prophet model flavor enables logging of Prophet models per MLflow format strada the mlflow.prophet.save_model() and mlflow.prophet.log_model() methods. These methods also add the python_function flavor esatto 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 molla. 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-per model persistence utilities are convenient for packaging models from various popular ML libraries durante MLflow Model format, they do not cover every use case. For example, you may want onesto use per 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 momento puro create an MLflow Model. Fortunately, MLflow provides two solutions that can be used onesto accomplish these tasks: Custom Python Models and Custom Flavors .