Forecast electricity demand with GluonTS and SageMaker custom containers

Learn how to train and evaluate time series models with the Gluon TS library, and then deploy them for inference as a Multi-Model Server SageMaker endpoint using a custom SageMaker container.

Level

• 300 Intermediate

Datasets

• UC Irvine Machine Learning Repository - Individual household electric power consumption

AWS services

• Amazon SageMaker
• AWS ECR
• Gluon TS

Architecture diagram

Getting Started

1. Set the the Conda virtual environment by excuting the command below (this process requires a bit long waiting time):

   ./build_env.sh

2. Run the Jupyter Notebooks (select the Kernel named conda_gluonts-multimodel).

   • 01_predict_electricity_demand_with_the_gluonts_library.ipynb
   • 02_deploy_gluonts_forecast_models_as_multi_model_endpoints.ipynb

Outline

• 1. Introduction
• 2. Problem definition
• 3. Architecture design
• 4. Data Discovery
• 5. Machine Learning Models
• 6. Prepare the model artifacts for deployment
• 7. How to build the custom Sagemaker container for model deployment
• 8. How to deploy models as Sagemaker Multi-model Endpoint and invoke the Endpoint
• 9. How to Do Batch Transform in the Multi Model Server Framework
• 10. Clean up the resources.
• 11. Conclusion

License

This library is licensed under the MIT-0 License. See the LICENSE file.