xgboost.save_model() and mlflow.xgboost.log_model() methods in 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 coraggio 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 preciso load MLflow Models with the xgboost model flavor mediante native XGBoost format.
LightGBM ( lightgbm )
The lightgbm model flavor enables logging of LightGBM models in MLflow format strada the dabble mlflow.lightgbm.save_model() and mlflow.lightgbm.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 via 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 to 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 cammino the mlflow.catboost.save_model() and mlflow.catboost.log_model() methods. 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 coraggio mlflow.pyfunc.load_model() . You can also use the mlflow.catboost.load_model() method puro load MLflow Models with the catboost model flavor per native CatBoost format.
Spacy( spaCy )
The spaCy model flavor enables logging of spaCy models con MLflow format strada 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 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.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 durante MLflow format via the mlflow.fastai.save_model() and mlflow.fastai.log_model() methods. Additionally, these methods add the python_function flavor sicuro 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.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 durante MLflow format strada the mlflow.statsmodels.save_model() and mlflow.statsmodels.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 cammino 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 durante 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 in MLflow format strada the mlflow.prophet.save_model() and mlflow.prophet.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 cammino mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame spinta. You can also use the mlflow.prophet.load_model() method to 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 mediante MLflow Model format, they do not cover every use case. For example, you may want puro use a model from an ML library that is not explicitly supported by MLflow’s built-con flavors. Alternatively, you may want onesto package custom inference code and scadenza preciso create an MLflow Model. Fortunately, MLflow provides two solutions that can be used to accomplish these tasks: Custom Python Models and Custom Flavors .
