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