Amazon SageMaker Autopilot currently allow deploying generated models to real-time inference endpoints by default. In this repository, we'll show how to deploy Autopilot models trained with ENSEMBLING and HYPERPARAMETER OPTIMIZATION (HPO) modes to serverless endpoints.
The notebook in this folder is the solution as described in this blog post.
In this example, we use the UCI Bank Marketing dataset to predict if a client will subscribe to a term deposit offered by the bank. This is a binary classification problem type.
In the first part of the notebook we'll launch two Autopilot jobs one with training mode set to ENSEMBLING and the other with HYPERPARAMETER OPTIMIZATION (HPO).
Autopilot generates a single model in ENSEMBLING training mode. We deploy this single model to a serverless endpoint. Then we also send an inference request with test data to the serverless endpoint.
In the second part of the notebook we'll extract the three inference containers generated by Autopilot in HPO training mode and deploy these models to three separate serverless endpoints and send inference requests in sequence.
- If you’re new to Autopilot, we encourage you to refer to Get started with Amazon SageMaker Autopilot.
- To determine the optimal configuration for your serverless endpoint from a cost and performance perspective, we encourage you to explore our Serverless Inference Benchmarking Toolkit. For more information, refer to Introducing the Amazon SageMaker Serverless Inference Benchmarking Toolkit.
- To learn more about Autopilot training modes, refer to Amazon SageMaker Autopilot is up to eight times faster with new ensemble training mode powered by AutoGluon.
- Refer to Inference container definitions for regression and classification problem types.
- For an overview on how to deploy an XGBoost model to a serverless inference endpoint, we encourage you to refer to this example notebook.
See CONTRIBUTING for more information.
This project is licensed under the MIT License.