AutoML is a powerful capability, provided by Amazon SageMaker Autopilot, that allows non-experts to create machine learning (ML) models to invoke in their applications.
The problem that we want to solve arises when, due to governance constraints, Amazon SageMaker resources can’t be deployed in the same AWS account where they are used.
Examples of such a situation are:

A multi-account enterprise setup of AWS where the Autopilot resources must be deployed in a specific AWS account (the trusting account), and should be accessed from trusted accounts
A software as a service (SaaS) provider that offers AutoML to their users and adopts the resources in the customer AWS account so that the billing is associated to the end customer

This post walks through an implementation using the SageMaker Python SDK. It’s divided into two sections:

Create the AWS Identity and Access Management (IAM) resources needed

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