A case-study of the lifecycle of a machine learning-based application within the Eclipse Kura ecosystem: from data collection to training, deployment and inference on the edge.
We'll focus on the process of creating a deep learning anomaly detector from scratch, leveraging the entire Eclipse Kura ecosystem:
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Data collection: Kura Wires for collecting diagnostic data from an appliance and upload on the Eclipse Kapua cloud
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Training: How can we download the collected data from Eclipse Kapua and develop a model within our preferred ML library (Tensorflow/Keras).
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Inference: How can we export the trained model for running on an Inference Server (Nvidia Triton™ Server) which will be our target runtime.
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Deployment: How can we deploy this trained model on the edge device leveraging the intuitive Eclipse Kura Wires interface and running the models with Eclipse Kura's inference engine service.
Exciting new and upcoming Eclipse Kura features for Edge AI (AI Model Encryption, New Inference Servers support and more).
