Deploy the custom Python code to Cloud Functions, and use Kubeflow Pipelines to trigger the Cloud Function. This approach leverages serverless computing to reduce operational overhead.

Package the custom Python code into Docker containers, and use the load_component_from_file function to import the containers into the pipeline. This method provides isolation and reproducibility but requires additional container management.

Use the predefined components available in the Kubeflow Pipelines SDK to access Dataproc, and run the custom code there. This option utilizes managed services for ease of use but may introduce latency and cost concerns.

Use the func_to_container_op function to create custom components from the Python code. This function simplifies the process by automatically generating a Docker container with all necessary dependencies, facilitating seamless integration with the Kubeflow Pipelines SDK.