Python API

The MLflow Python API is organized into the following modules. The most common functions are exposed in the mlflow module, so we recommend starting there.

• mlflow
• mlflow.artifacts
• mlflow.azureml
• mlflow.catboost
• mlflow.client
• mlflow.deployments
• mlflow.diviner
• mlflow.entities
• mlflow.environment_variables
• mlflow.fastai
• mlflow.gluon
• mlflow.h2o
• mlflow.keras
• mlflow.lightgbm
• mlflow.mleap
• mlflow.models
• mlflow.onnx
• mlflow.paddle
• mlflow.pipelines
  • Regression Pipeline
• mlflow.pmdarima
• mlflow.projects
• mlflow.prophet
• mlflow.pyfunc
  • Inference API
  • Filesystem format
    • MLModel configuration
  • Creating custom Pyfunc models
    • Workflows
    • Which workflow is right for my use case?
• mlflow.pyspark.ml
• mlflow.pytorch
• mlflow.sagemaker
• mlflow.shap
• mlflow.sklearn
• mlflow.spacy
• mlflow.spark
• mlflow.statsmodels
• mlflow.tensorflow
• mlflow.types
• mlflow.xgboost

See also the index of all functions and classes.