About Edge Impulse
Edge Impulse is a platform for developing and running machine learning on edge hardware. It provides end-to-end pipelines, security, versioning and model monitoring so teams can build edge AI and physical AI solutions and deploy them to devices at scale.
It targets a range of hardware including microcontrollers, neural processing units, CPUs and GPUs, as well as gateways, sensors and cameras, and can deploy as Docker containers. Application areas include computer vision, asset tracking and monitoring, human interfaces and predictive maintenance, with a Visual Inspection Suite for inspection use. Integrations cover partners such as Arduino, Qualcomm and NVIDIA.
Edge Impulse is a hosted commercial platform with documentation, a forum and programs for students and educators. Plans are listed on its pricing page.
Key features
- End-to-end edge ML pipelines
- Model versioning and monitoring
- Deployment to microcontrollers, NPUs and GPUs
- Docker container deployment
- Visual Inspection Suite
- Integrations with Arduino, Qualcomm and NVIDIA
Good fit for
- →Running predictive maintenance models on sensors
- →Deploying computer vision to edge cameras
- Tags
- edge-ai
- mlops
- machine-learning
- embedded
- computer-vision
- iot
- predictive-maintenance
Edge Impulse: questions and answers
- What is Edge Impulse used for?
- Edge Impulse is an MLOps platform for building, deploying and monitoring machine learning models on edge devices and embedded hardware. It is a good fit for running predictive maintenance models on sensors and deploying computer vision to edge cameras.
- Is Edge Impulse free?
- Yes. Edge Impulse has a free plan.
- Is Edge Impulse open source?
- No. Edge Impulse is proprietary (closed-source) software. In the AI Infrastructure category, open-source options include Label Studio, MLflow and Superduper.
- What are some alternatives to Edge Impulse?
- Edge Impulse competes with Roboflow, Landing AI and NVIDIA AI Enterprise.
Open-source alternatives to Edge Impulse
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🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
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