The xgboost model flavor enables logging of XGBoost models mediante MLflow format modo the mlflow

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

LightGBM ( lightgbm )

The lightgbm model flavor enables logging of LightGBM models durante MLflow format inizio 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 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.lightgbm.load_model() method esatto load MLflow Models with the lightgbm model flavor mediante native LightGBM format.

CatBoost ( catboost )

The catboost model flavor enables logging of CatBoost models mediante MLflow format modo the mlflow.catboost.save_model() and mlflow.catboost.log_model() methods. These methods also add the python_function flavor preciso 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 esatto load MLflow Models with the catboost model flavor sopra native CatBoost format.

Spacy( spaCy )

The spaCy model flavor enables logging of spaCy models durante MLflow format cammino the mlflow.spacy.save_model() and mlflow.spacy.log_model() methods. Additionally, these methods add the python_function flavor esatto the MLflow Models that they produce, allowing the models esatto be interpreted as generic Python functions for inference strada mlflow.pyfunc.load_model() . Esempi di profilo eharmony 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 per MLflow format inizio 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 sicuro 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.fastai.load_model() method puro load MLflow Models with the fastai model flavor in native fastai format.

Statsmodels ( statsmodels )

The statsmodels model flavor enables logging of Statsmodels models durante MLflow format modo 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 sicuro be interpreted as generic Python functions for inference modo 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 sicuro 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 a call onesto fit on verso statsmodels model.

Prophet ( prophet )

The prophet model flavor enables logging of Prophet models sopra MLflow format coraggio 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 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.prophet.load_model() method esatto load MLflow Models with the prophet model flavor con native prophet format.

Model Customization

While MLflow’s built-con model persistence utilities are convenient for packaging models from various popular ML libraries per MLflow Model format, they do not cover every use case. For example, you may want to use verso model from an ML library that is not explicitly supported by MLflow’s built-sopra flavors. Alternatively, you may want sicuro package custom inference code and data sicuro create an MLflow Model. Fortunately, MLflow provides two solutions that can be used sicuro accomplish these tasks: Custom Python Models and Custom Flavors .