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

xgboost.save_model() and mlflow.xgboost.log_model() methods per python and mlflow_save_model and mlflow_log_model sopra R respectively. 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 cammino 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 puro load MLflow Models with the xgboost model flavor in native XGBoost format.

LightGBM ( lightgbm )

The lightgbm model flavor enables logging of LightGBM models sopra MLflow format cammino the mlflow.lightgbm.save_model() and mlflow.lightgbm.log_model() methods. These methods also add the python_function flavor to 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 input. 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 durante MLflow format coraggio 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 esatto be interpreted as generic Python functions for inference strada 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 mediante MLflow format cammino the mlflow.spacy.save_model() and mlflow.spacy.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 modo mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame spinta. You can also use the mlflow.spacy.load_model() method esatto load MLflow Models with the spacy model flavor mediante 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 inizio 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 con native fastai format.

Statsmodels ( statsmodels )

The statsmodels model flavor enables logging of Statsmodels models sopra MLflow format coraggio the mlflow.statsmodels.save_model() and mlflow.statsmodels.log_model() methods. These methods also add the python_function flavor preciso the MLflow Models that they produce, allowing the models puro be interpreted as generic Python functions for inference inizio mlflow.pyfunc.load_model() . This loaded PyFunc model can only be scored with DataFrame spinta. You can also use the mlflow.statsmodels.load_model() method onesto load MLflow Models with the statsmodels model flavor con native statsmodels format.

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

Prophet ( prophet )

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

Model Customization

While MLflow’s built-con 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 onesto use per model from an ML library that is not explicitly supported by MLflow’s built-in flavors. Alternatively, you may want sicuro package custom inference code and data puro create an MLflow Model. Fortunately, MLflow provides two solutions that can be used onesto accomplish these tasks: Custom https://datingranking.net/it/cybermen-review/ Python Models and Custom Flavors .