On-device learning, also known as on-device training, involves training or fine-tuning a machine learning model directly on the device where it is deployed, using real-time data from sensors. While on-device learning can be useful in specific scenarios, its utility is often limited due to the dependency on labeled data, which can be challenging (or impossible) to obtain in a deployed environment.Documentation Index
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On-device learning with Edge Impulse
Edge Impulse provides several solutions for different on-device learning scenarios:- On-device learning for anomaly detection: Utilizes unsupervised learning to establish a “normal” baseline on-device, enabling the detection of anomalies in time series and visual data.
- Zero-shot prompt modification with vision language models (VLMs): Allows developers to reconfigure vision models on-device by modifying prompts, facilitating real-time customization for vision applications.
- Edge Impulse on-premise appliance: Enables customers to run the entire Edge Impulse platform on their hardware. This setup supports model training near production devices with human oversight for labeling.