tinyML Talks: Advanced Anomaly Detection Made Easy

Being able to detect anomalies is becoming an extremely useful technique in the world of embedded ML, and one that can be done on the most constrained always-on devices. Anomaly detection can be used for a multitude of use cases, from cold chain monitoring to fault detection on industrial machinery or satellites.

In this talk we will present how to use data-driven engineering to create your data set and use Edge Impulse to create a model able to classify anomalous sensor readings. We will do this by leveraging some new powerful features in Edge Impulse.

You will learn to:
· Implement and use custom DSP blocks to analyze your IoT data and extract the most important features
· harness the value of feature importance to zoom in on interesting frequency bands
· iterate over thresholds in anomaly detection blocks, in order to find the optimal configuration


February 15, 2022



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Timezone: PST

Advanced Anomaly Detection Made Easy

Jenny PLUNKETT, Senior Developer Relations Engineer

Edge Impulse

Jenny PLUNKETT, Senior Developer Relations Engineer

Edge Impulse

Jenny Plunkett is a Texas Longhorn and software engineer, working as a Senior User Success Engineer at Edge Impulse. Since graduating from The University of Texas she has been working in the IoT space, from customer engineering and developer support for Arm Mbed to consulting engineering for the Pelion device management platform.

Schedule subject to change without notice.