BYOM using a SageMaker endpoint for remote inference
In this section, we will explore how to create a BYOM remote inference for an Amazon SageMaker Random Cut Forest model. This means you are bringing your own machine learning model, which is trained on data outside of Redshift, and using it to make predictions on data stored in a Redshift cluster using an endpoint. In this method, to use BYOM for remote inference, a machine learning model is trained, an endpoint is created in Amazon SageMaker, and then the endpoint is accessed from within a Redshift query using SQL functions provided by the Amazon Redshift ML extension.
This method is useful when Redshift ML does not natively support models, for example, a Random Cut Forest model. You can read more about Random Cut Forest here: https://tinyurl.com/348v8nnw.
To demonstrate this feature, you will first need to follow the instructions found in this notebook (https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms...
