Anomaly Detection on Remote Sensing with Ray + Horovod

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Labeling remote sensing data is crucial for supervised ML/AI in Earth Science, but also very challenging. Manual labeling is not practical as the volume of remote sensing data is tremendous. In this talk, Linsong will show how Ray's distributed learning integration with Horovod can be used to train AI models at scale for identifying "anomalous" regions in an unsupervised manner. Linsong will demonstrate this workflow on a sample application, disaster classification and illustrate it with open source Sentinel satellite data. Ray and Horovod running on IBM's serverless platform Cloud Code Engine enable the scalable learning and inferencing across a few terabytes of raw data.
The attendees will be able to see a demo of this end to end workflow on open source datasets.