How Amazon trains sequential ensemble models at scale with Amazon SageMaker Pipelines
CAT
1 min read
Ensemble models are becoming popular within the ML communities. They generate more accurate predictions through combining the predictions of multiple models. Pipelines can quickly be used to create and end-to-end ML pipeline for ensemble models. This enables developers to build highly accurate models while maintaining efficiency, and reproducibility. In this post, we provide an example of an ensemble model that was trained and deployed using Pipelines.
Originally appeared here:
How Amazon trains sequential ensemble models at scale with Amazon SageMaker Pipelines
