Amazon SageMaker Pipelines is the first purpose-built CI/CD service for machine learning (ML). It helps you build, automate, manage, and scale end-to-end ML workflows and apply DevOps best practices of CI/CD to ML (also known as MLOps).
Creating multiple accounts to organize all the resources of your organization is a good DevOps practice. A multi-account strategy is important not only to improve governance but also to increase security and control of the resources that support your organization’s business. This strategy allows many different teams inside your organization, to experiment, innovate, and integrate faster, while keeping the production environment safe and available for your customers.
Pipelines makes it easy to apply the same strategy to deploying ML models. Imagine a use case in which you have three different AWS accounts, one for each environment: data science, staging, and production. The data scientist has the freedom to run experiments and train and optimize different models any time in their own account. When a model is good enough to be deployed in production, the data scientist just needs to flip the model approval status to
Approved. After that, an automated process deploys the model on the staging account. Here you can automate testing of the model with unit tests or integration tests or test the model manually. After a manual or automated approval, the model is deployed to the production account, which is a more tightly controlled environment used to serve inferences on real-world data. With Pipelines, you can implement a ready-to-use multi-account environment.
In this post, you learn how to use Pipelines to implement your own multi-account ML pipeline. First, you learn how to configure your environment and prepare it to use a predefined template as a SageMaker project for training and deploying a model in two different accounts: staging and production. Then, you see in detail how this custom template was created and how to create and customize templates for your own SageMaker projects.
Preparing the environment
In this section, you configure three different AWS accounts and use SageMaker Studio to create a project that integrates a CI/CD pipeline with the ML pipeline created by a data scientist. The following diagram shows the reference architecture of the environment that is created by the SageMaker custom project and how AWS Organizations integrates the different accounts.
The diagram contains three different accounts, managed by Organizations. Also, three different user roles (which may be the same person) operate this environment:
- ML engineer – Responsible for provisioning the SageMaker Studio project that creates the CI/CD pipeline, model registry, and other resources
- Data scientist – Responsible for crea
Source - Continue Reading: https://aws.amazon.com/blogs/machine-learning/multi-account-model-deployment-with-amazon-sagemaker-pipelines/