Register a pipeline to mlflow with KedroPipelineModel custom mlflow model

You can log a Kedro Pipeline to mlflow as a custom model through the CLI with modelify command:

kedro mlflow modelify --pipeline=<your-pipeline> --input-name <name-in-catalog-of-input-data>

This command will create a new run with an artifact named model. Open the user interface with kedro mlflow ui to check the result. You can also:

• specify the run id in which you want to log the pipeline with the --run-id argument, and its name with the –run-name argument.

• pass almost all arguments accepted by mlflow.pyfunc.log_model, see the list of all accepted arguments in the API documentation