SageMaker supports various ML algorithms. You can also create your proprietary ML model and deploy it on SageMaker. For example, cluster analysis would likely be trained on your own data because you want to categorize new records into the same categories that you used for previous records. Alternatively, for predicting real-world sports events, you could use a publicly available model because the training data used would be in the public domain.
We expect that domain- or industry-specific predictions will typically be trained on your own data in SageMaker, while undifferentiated ML needs such as machine translation will use external models.