Deployment

In this section we provide guides for different deployment methods; your choice will depend on a range of factors.

If you decide to deploy your Kedro project onto a single machine, you should consult our guide to single-machine deployment, and decide whether to:

• use Docker for container-based deployment

• use package-based deployment

• use the CLI to clone and deploy your codebase to a server

If your pipeline is sizeable, you may want to run it across separate machines, so will need to consult our guide to distributed deployment.

This following pages provide information for deployment to, or integration with, the following:

• Airflow

• Amazon SageMaker

• Amazon EMR Serverless

• AWS Step functions

• Azure

• Dask

• Databricks

• Kubeflow Workflows

• Prefect

• Vertex AI

Warning

We also have legacy documentation pages for the following deployment targets, but these have not been tested against recent Kedro releases and we cannot guarantee them:

• for Argo Workflows

• for AWS Batch