Is It Time To Take Control Of Your Edge AI Project? A Conversation With SensiML EDGE AI POD podcast

Is it time to take control of your Edge AI Project? A conversation with SensiML

9M ago 56:07

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Ready to unlock the secrets behind real-time sensing algorithms for embedded devices? In this episode, you’ll gain valuable insights into transforming raw sensor data into actionable intelligence. We begin by breaking down the complexities of signal preprocessing, event triggering mechanisms, and feature extraction. Learn how event detection can drastically reduce false positives and computational overhead, setting the stage for robust and efficient real-time systems.
Next, we delve into the world of AutoML tailored for embedded devices. Discover the nuances of hyperparameter tuning, cross-fold validation, and model ranking based on key performance metrics like F1 score and accuracy. We also introduce you to Piccolo AI, an open-source game-changer for model building and sensor data management. Plus, we answer listener questions about practical implementation details, making this segment a must-listen for any embedded systems enthusiast.
Finally, get hands-on with Piccolo AI as we guide you through setting up your environment using Docker and exploring its powerful web interface. From adding new feature extractors to using synthetic data for model validation, we cover everything you need to contribute to or leverage this open-source project. We also emphasize community engagement, highlighting how collaborative efforts can drive innovation and improve the versatility of machine learning in embedded systems. Join us and become part of a thriving community pushing the boundaries of what's possible.

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Hoofdstukken

1. Real-Time Sensing Algorithm for Embedded Devices (00:00:00)

2. Applying AutoML in Embedded Devices (00:10:13)

3. Getting Started With Piccolo AI (00:13:55)

4. Adding Feature Extractors in Piccolo AI (00:28:41)

5. Data Studio Features and Community Engagement (00:39:10)

6. Model Validation and Synthetic Data (00:49:11)

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