(how to customize this repo for running your ML inference task)

The primary functionality of this repository is contained in the lambda function located at lambda/download_and_predict/handler.py. It is intentionally very little code to allow for easy configuration: with the imports and assertions removed, there is one class instantiation and four method calls. The class DownloadAndPredict provides the base functionality required to run machine learning inference:

• Creates a list of Mercator tiles based on an input SQS event.
• Downloads those tiles from a TMS/XYS tile endpoint and puts them in the proper format for sending them to Tensorflow Serving or an equivalent Docker image.
• Sends the payload to the prediction endpoint.
• Saves the result into a database.

There are two primary options to customize this workflow:

• Add new code to handler.py to manipulate the returned values (tiles, payload, content, etc.)
• Subclass DownloadAndPredict to provide alternative methods for the operations listed above.

Any additional third-party libraries should be added to lambda/setup.py for inclusion in the lambda function deployment.

Examples of customization are listed in this library to show how chip-n-scale-queue-arranger can be used with a variety of different tools.

• Download imagery from Sentinel Hub. For more information, check out the sentinelhub-py docs.
• Download larger tiles and create smaller tiles for inference. This is useful for reducing the load on the imagery/tile endpoint.
• Save results to ml-enabler. For more information, check out the ml-enabler repo.