AWS

How to set up stacks on Amazon Web Services (AWS)

This is an older version of the ZenML documentation. To read and view the latest version please visit this up-to-date URL.

AWS is one of the most popular cloud providers and offers a range of services that can be used while building your MLOps stacks. You can learn more about machine learning at AWS on their website.

Available Stack Components

This is a list of all supported AWS services that you can use as ZenML stack components.

Elastic Kubernetes Service (EKS)

Amazon Elastic Kubernetes Service (Amazon EKS) is a managed container service to run and scale Kubernetes applications in the cloud or on-premises. Learn more here.

• An EKS cluster can be used to run multiple orchestrators.

  • A Kubernetes-native orchestrator.

  • A Kubeflow orchestrator.

• You can host model deployers on the cluster.

  • A Seldon model deployer.

  • An MLflow model deployer.

• Experiment trackers can also be hosted on the cluster.

  • An MLflow experiment tracker

Simple Storage Service (S3)

Amazon Simple Storage Service (Amazon S3) is an object storage service that offers scalability, data availability, security, and performance. Learn more here.

• You can use an S3 bucket as an artifact store to hold files from our pipeline runs like models, data and more.

Elastic Container Registry (ECR)

Amazon Elastic Container Registry (Amazon ECR) is an AWS managed container image registry service that is secure, scalable, and reliable. Learn more here.

• An ECS registry can be used as a container registry stack component to host images of your pipelines.

SageMaker

Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly build and train machine learning models, and then directly deploy them into a production-ready hosted environment. Learn more here.

• You can use SageMaker as a step operator to run specific steps from your pipeline using it as the backend.

Relational Database Service (RDS)

Amazon Relational Database Service (Amazon RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the AWS Cloud. Learn more here.

• You can use Amazon RDS as a metadata store to track metadata from your pipeline runs.

Secrets Manager

Secrets Manager enables you to replace hardcoded credentials in your code, including passwords, with an API call to Secrets Manager to retrieve the secret programmatically. Learn more here.

• You can store your secrets to be used inside a pipeline by registering the AWS Secrets Manager as a ZenML secret manager stack component.

In the following pages, you will find step-by-step guides for setting up some common stacks using the AWS console and the CLI. More combinations and components are progressively updated in the form of new pages.