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Supported Amazon SageMaker models

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Learn more about the Amazon SageMaker models to which you can connect in Prediction Studio.

Supported Amazon SageMaker models

You can connect to Amazon SageMaker models that use the following algorithms:

  • TensorFlow
  • XGBoost
  • K-means
  • K-nearest neighbors
  • Linear learner
  • Random cut forest

You can also connect to an Amazon SageMaker model that uses a custom algorithm. To connect to a custom model, configure the Amazon SageMaker docker container. For more information, see the Amazon Web Services documentation.

Supported input and output formats for Amazon SageMaker models

The supported input and output formats depend on the model algorithm. Consult the following table to learn more about the supported input and output formats for supported models:

Supported input and output formats for Amazon SageMaker models

Model algorithm Supported input format Supported output format
TensorFlow CSV CSV
XGBoost CSV JSON
K-means CSV JSON
K-nearest neighbors CSV JSON
Linear learner CSV JSON
Random cut forest CSV JSON

  • Connecting to an external model

    You can run your custom artificial intelligence (AI) and machine learning (ML) models externally in third-party machine learning services. This way, you can implement custom predictive models in your decision strategies by connecting to models in the Google AI Platform and Amazon SageMaker machine learning services.

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