The xgboost model flavor enables logging of XGBoost models durante MLflow format cammino the mlflow

xgboost.save_model() and mlflow.xgboost.log_model() methods mediante python and mlflow_save_model and mlflow_log_model mediante R respectively. These methods also add the python_function flavor puro the MLflow Models that they produce, allowing the models sicuro 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 preciso load MLflow Models with the xgboost model flavor mediante native XGBoost format.

LightGBM ( lightgbm )

The lightgbm model flavor enables logging of LightGBM models durante MLflow format cammino the mlflow.lightgbm.save_model() and mlflow.lightgbm.log_model() methods. These methods also add the python_function flavor puro the MLflow Models that they produce, allowing the models puro 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 esatto load MLflow Models with the lightgbm model flavor in native LightGBM format.

CatBoost ( catboost )

The catboost model flavor enables logging of CatBoost models per MLflow format inizio 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 preciso be interpreted as generic Python functions for inference inizio mlflow.pyfunc.load_model() . You can also use the mlflow.catboost.load_model() method preciso load MLflow Models with the catboost model flavor per native CatBoost format.

Spacy( spaCy )

The spaCy model flavor enables logging of spaCy models mediante MLflow format inizio 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 preciso 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 onesto load MLflow Models with the spacy model flavor sopra native spaCy format.

Fastai( fastai )

The fastai model flavor enables logging of fastai Learner models in MLflow format cammino the mlflow.fastai.save_model() and mlflow.fastai.log_model() methods. Additionally, these methods add the python_function flavor esatto 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.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 durante MLflow format cammino 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 preciso 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.statsmodels come funziona iamnaughty.load_model() method to load MLflow Models with the statsmodels model flavor sopra native statsmodels format.

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

Prophet ( prophet )

The prophet model flavor enables logging of Prophet models durante MLflow format via the mlflow.prophet.save_model() and mlflow.prophet.log_model() methods. These methods also add the python_function flavor puro 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.prophet.load_model() method onesto load MLflow Models with the prophet model flavor con native prophet format.

Model Customization

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