Using Chalice to serve SageMaker predictions – Julien Simon – Medium

Amazon SageMaker makes it easy to train and deploy Machine Learning models hosted on HTTP endpoints. However, in most cases you won't expose these endpoints directly. Pre-processing and post-processing steps are likely to be required: authentication, throttling, data transformation and enrichment, logging, etc. In this post, we will use AWS Chalice to build a web service acting as a front-end for a SageMaker endpoint. I've already written a couple of posts (here and here) on training and deploying SageMaker models, so I won't go into these details again.