Run Linux based IoT Edge modules on Windows IoT

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Start free todayIoT devices can work with cognitive services in the cloud for ML tasks such as face verification. However, it is often useful to have ML at the edge as well, to avoid streaming up data all the time – like a "wake word". We will show you how Azure Sphere enables you to easily build ML at the edge that works with ML in the cloud, in the context of a face detection/recognition scenario.
Learn more reading the blog post at https://aka.ms/iotshow/MLOnAzureSphere
Hi,
Do you have any hybrid ML examples using Azure Sphere and Cognitive Services for recognizing/verifying voices (instead of faces)?
@jamesscott,
Thank you for your reply. The context of my question is to perform research and a proof of concept (using the MT3620 Azure Sphere Starter Kit from AvNet) for the following use cases in healthcare:
1) An end user uses their voice to authenticate to a remote patient monitoring device in their home. Today's commercial solutions simply use a wake word where basically anyone can impersonate the end user.
2) Voice based authentication in a clinical scenario where healthcare workers can have a low friction user experience for biometric based authentication into Azure Sphere (and by extension local IoT capabilities and cloud capabilities). The usage of PPE make conventional biometric authentication (face, finger) difficult to use.
I am wondering if there can be a good mix of TinyML on the Azure Sphere device with more robust AI/ML capabilities in the Azure cloud when needed.
Does that help provide further context?