easily create, deploy, and monitor mobile AI models
summary
Edge Impulse Studio offers a free dev tier with two private projects and claims a 200,000-developer community across 500,000 projects, and it turns labeled images or accelerometer traces into deployable models (keyword spotting in ~5 minutes, fall dataset in ~8). The workflow exports a TensorFlow Lite or C++ SDK binary/EIM, uses the NDK and JNI for Kotlin apps, and supports Qualcomm QNN; the demo builds a visual anomaly detection app and a Wear OS fall detection template that runs on a Pixel 3.
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chapters (13)
- 0:00Introduction
- 1:25Today’s goal and overview
- 3:12Why run AI on device
- 7:05Edge AI benefits and platform
- 9:11What is Edge Impulse
- 11:58Getting started with templates
- 16:52Live demo setup and walkthrough
- 19:14Collecting and labeling data
- 21:22Designing the ML architecture
- 24:49Training and deployment options
- 26:26Testing and final deployment
- 32:44Real-world use cases and scaling
- 34:31Q&A
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