Democratizing AI Development with Edge Impulse
The democratization of development tools such as Edge Impulse enables designers to create objects imbued with AI-based capabilities.
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The democratization of development tools such as Edge Impulse enables designers to create objects imbued with AI-based capabilities.
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In part one of the Edge Impulse Fundamentals series, we focus on capturing the data that drives machine learning models.
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We take a practical look at the overall Edge Impulse workflow, from data collection and training to firmware deployment on targeted edge devices.
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We focus on the pre-processing pipeline in impulse design.
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We look at the learning and output blocks that create the impulse workflow.
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We look at the processes and tools Edge Impulse provides to analyze ML model performance.
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We finish exploring the tools Edge Impulse provides to analyze ML model performance and investigate ways to mitigate the effects of overfitting and underfitting.
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We explore how the live classification tool ensures the model is ready for real-world deployment.
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We delve into how Edge Impulse manages model versioning and deployment.
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Find out how to measure motor vibrations using the Sensor Featherwing and create a Machine Learning algorithm on the Portenta board to detect anomalies.
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A genuinely interoperable Internet of Things requires devices to interact in ways that are transparent to end users.
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Older equipment often lacks the digital intelligence of new machines. Machine Learning (ML) technology can be used to retrofit legacy machinery with new features.
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Healthcare is a field that stands to benefit greatly from machine learning on the edge.
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Examine how Conservation X Labs worked closely with Edge Impulse to jointly develop solutions for bringing edge AI to the wildlife conservation field.
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