AWS Sagemaker Orchestrator

Orchestrating your pipelines to run on Amazon Sagemaker.

Sagemaker Pipelines is a serverless ML workflow tool running on AWS. It is an easy way to quickly run your code in a production-ready, repeatable cloud orchestrator that requires minimal setup without provisioning and paying for standby compute.

This component is only meant to be used within the context of a remote ZenML deployment scenario. Usage with a local ZenML deployment may lead to unexpected behavior!

When to use it

You should use the Sagemaker orchestrator if:

you're already using AWS.

you're looking for a proven production-grade orchestrator.

you're looking for a UI in which you can track your pipeline runs.

you're looking for a managed solution for running your pipelines.

you're looking for a serverless solution for running your pipelines.

How it works

The ZenML Sagemaker orchestrator works with Sagemaker Pipelines, which can be used to construct machine learning pipelines. Under the hood, for each ZenML pipeline step, it creates a SageMaker PipelineStep, which contains a Sagemaker Processing job. Currently, other step types are not supported.

How to deploy it

In order to use a Sagemaker AI orchestrator, you need to first deploy ZenML to the cloud. It would be recommended to deploy ZenML in the same region as you plan on using for Sagemaker, but it is not necessary to do so. You must ensure that you are connected to the remote ZenML server before using this stack component.

The only other thing necessary to use the ZenML Sagemaker orchestrator is enabling the relevant permissions for your particular role.

In order to quickly enable APIs, and create other resources necessary for to use this integration, we will soon provide a Sagemaker stack recipe via our mlstacks repository, which will help you set up the infrastructure with one click.

Infrastructure Deployment

A Sagemaker orchestrator can be deployed directly from the ZenML CLI: