I have a usecase for hosting multiple xgboost models in one sagemaker endpoint. The models have a slightly different feature set and preprocessing for features.
The two options I am considering are:
Creating models with custom docker images and hosting them in one endpoint using production variants. I will then invoke the endpoint with the variant name and correct feature set.
Sagemaker Inference Toolkit (multi-model-server). In the handler script I am planning to pre-process the input differently based on the model name.
Are these the right approach for the problem? Or is there a better approach for working with Sagemaker and multiple xgboost model with pre and post processing?