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MCUs, NPUs, CPUs, GPUs. Build datasets, train models, and optimize libraries to run directly on device; from the smallest microcontrollers to gateways with the latest neural accelerators (and anything in between). The world’s most innovative companies choose Edge Impulse.
Edge Impulse is the leading edge AI platform for collecting data, training models, and deploying them to your edge computing devices. It provides an end-to-end framework that easily plugs into your edge MLOps workflow.
Welcome to Edge Impulse! We enable professional developers and researchers to create the next generation of intelligent products with Edge AI. In this documentation, you'll find user guides, tutorials, and API documentation.
Edge Impulse empowers ML teams to run AI at peak performance on any edge hardware, with unmatched ease and speed.
We’re building the future of data-driven engineering. Edge Impulse is ushering in the future of embedded machine learning by empowering developers to create and optimize solutions with real-world data.
Edge machine learning (edge ML) is the process of running machine learning algorithms on computing devices at the periphery of a network to make decisions and predictions as close as possible to the originating source of data. It is also referred to as edge artificial intelligence or edge AI.
Edge Impulse empowers you to bring intelligence to your embedded projects by enabling devices to understand and respond to their environment. Whether you want to recognize sounds, identify objects, or detect motion, Edge Impulse makes it accessible and straightforward.
Case Studies. Embedded ML solutions, with Edge Impulse. Edge Impulse puts the power of ML into real products, across every industry. Asset tracking & monitoring. Industrial Asset Tracking with NVIDIA Omniverse and TAO. In this case study, we will look at pallet counting, a key part of global shipping and logistics systems. Health.
Explore the benefits of shifting workloads from the cloud to the edge, turning data from any device into actionable insights. Expand your data science operations. Find new ways to optimize your ML and data science capabilities and scale your edge ML infrastructure and deployments. Quick overview of Key speakers.
What you'll learn. The basics of a machine learning system. How to deploy a machine learning model to a microcontroller. How to use machine learning to make decisions and predictions in an embedded system.
