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Amazon Forecast Samples

Workshops, Notebooks and examples on how to learn and use various features of Amazon Forecast

Announcements and New Service Features

Introduction and Best Practices

Please visit our growing library which serves as a guide for onboarding data and learning how to use Amazon Forecast.

MLOps: Run a proof of concept (PoC) and learn how to automate production workloads

MLOps Automation

The purpose of this guidance is to provide customers with a complete end-to-end workflow that serves as an example -- a model to follow. As delivered, the guidance creates forecasted data points from an open-source input data set. The template can be used to create Amazon Forecast Dataset Groups, import data, train machine learning models, and produce forecasted data points, on future unseen time horizons from raw data. All of this is possible without having to write or compile code. Get Started Here

Notebooks

Here you will find examples how to use Amazon Forecast Python SDK to make API calls, with manual waits between API calls. Primary audience is Developers, MLOps Enginners, and Integration Partners who need to see how to put forecasts into production.

License Summary

This sample code is made available under a modified MIT license. See the LICENSE file.