Enterprise IoT edge processing with TensorFlow and Apache MXNet on the edge with Raspberry Pi, Movidius, Sense-Hat, Python, MiniFi, and NiFi.
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Sending Data to an advanced analytics platform like Zoomdata is child’s play. I added that as a live stream via REST while I am sending the JSON to be converted to AVRO then ORC for storage and Hive queries.
As part of the ingest, we store the images and make a current image for display.
See here and here for more details.
And below we see an overview of a flow to ingest both Apache MXNet + SenseHat Data as well as multiple rows of TensorFlow data
Then, we split our TensorFlow JSON data into individual JSON records.
This is how to split an array of JSON that doesn’t have a top element.
GitHub:
https://github.com/tspannhw/OpenSourceComputerVision
https://github.com/tspannhw/ApacheDeepLearning101
https://github.com/tspannhw/nifi-tensorflow-processor
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