The xgboost model flavor enables logging of XGBoost models in MLflow format inizio the mlflow

xgboost.save_model() and mlflow.xgboost.log_model() methods con python and mlflow_save_model and mlflow_log_model durante R respectively. These methods also add the python_function flavor to the MLflow Models that they produce, allowing the models preciso be interpreted as generic Python functions for inference strada mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame molla. You can also use the mlflow.xgboost.load_model() method esatto load MLflow Models with the xgboost model flavor mediante native XGBoost format.

LightGBM ( lightgbm )

The lightgbm model flavor enables logging of LightGBM models per MLflow format strada 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 esatto be interpreted as generic Python functions for inference cammino mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame stimolo. You can also use the mlflow.lightgbm.load_model() method esatto load MLflow Models with the lightgbm model flavor durante native LightGBM format.

CatBoost ( catboost )

The catboost model flavor enables logging of CatBoost models sopra MLflow format strada the mlflow.catboost.save_model() and mlflow.catboost.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 strada mlflow.pyfunc.load_model() . You can also use the mlflow.catboost.load_model() method sicuro load MLflow Models with the catboost model flavor per native CatBoost format.

Spacy( spaCy )

The spaCy model flavor enables logging of spaCy models per MLflow format modo the mlflow.spacy.save_model() and mlflow.spacy.log_model() methods. Additionally, these methods add the python_function flavor onesto the MLflow Models that they produce, allowing the models esatto be interpreted as generic Python functions for inference via 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 con 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 esatto be interpreted as generic Python functions for inference strada 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 preciso load MLflow Models with the fastai model flavor in native fastai format.

Statsmodels ( https://datingranking.net/it/coffee-meets-bagel-review/ 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 to the MLflow Models that they produce, allowing the models esatto 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.statsmodels.load_model() method to load MLflow Models with the statsmodels model flavor durante native statsmodels format.

As for now, automatic logging is restricted preciso parameters, metrics and models generated by verso call sicuro fit on per statsmodels model.

Prophet ( prophet )

The prophet model flavor enables logging of Prophet models in MLflow format via 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 modo 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 onesto load MLflow Models with the prophet model flavor mediante native prophet format.

Model Customization

While MLflow’s built-in 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 to use per model from an ML library that is not explicitly supported by MLflow’s built-mediante flavors. Alternatively, you may want onesto package custom inference code and scadenza to create an MLflow Model. Fortunately, MLflow provides two solutions that can be used puro accomplish these tasks: Custom Python Models and Custom Flavors .