This how-to will help you to integrate Amazon SageMaker AI with Teradata. The approach this guide explains is one of many potential approaches to integrate with the service.
Amazon SageMaker AI provides a fully managed Machine Learning Platform. There are two use cases for Amazon SageMaker AI and Teradata:
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Data resides on Teradata and Amazon SageMaker AI will be used for both the Model definition and subsequent scoring. Under this use case Teradata will provide data into an Amazon S3 bucket so that Amazon SageMaker AI can consume training and test data sets for the purpose of model development. Teradata would further make data available via Amazon S3 for subsequent scoring by Amazon SageMaker AI. Under this model Teradata is a data repository only.
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Data resides on Teradata and Amazon SageMaker AI will be used for the Model definition, and Teradata for the subsequent scoring. Under this use case Teradata will provide data into an Amazon S3 bucket so that Amazon SageMaker AI can consume training and test data sets for the purpose of model development. Teradata will need to import the Amazon SageMaker AI model into a Teradata table for subsequent scoring. Under this model Teradata is a data repository and a scoring engine.
The first use case is discussed in this document.
Amazon SageMaker AI consumes training and test data from an Amazon S3 bucket. This article describes how you can load Teradata analytics data sets into an Amazon S3 bucket. The data can then be available to Amazon SageMaker AI to build and train machine learning models and deploy them into a production environment.