Edge Impulse

Enables AI development for MCUs, NPUs, CPUs, GPUs, gateways, sensors, cameras, and Docker containers.

Allows users to create and manage datasets.

Provides capabilities to train machine learning models.

Optimizes libraries to run directly on devices.

Brings AI capabilities to edge devices.

Helps in extracting insights from sensor data.

Accelerates the delivery of next-generation products and solutions.

Simplifies AI development by removing hidden complexities and repetitive steps.

Enables quick progress on tasks leading to commercialization.

Offers agnostic and scalable edge AI tools to reduce development risks.

Addresses issues like downtime, inefficiency, and quality control through early anomaly detection.

Fast-tracks development and increases successful edge AI deployment.

Provides solutions for smart city intersections, vehicle safety, railway safety, and more.

Adds new insights to sensor networks through embedded machine learning for industrial enterprises.

Capable of processing various data types (time-series, audio, image data).

Supports various model types and training techniques.

Facilitates collaboration for production-ready models.

Offers a graphical interface for data collection, impulse building, and deployment.

Manages storing, sorting, and labeling of data.

Allows creation of impulses with feature extraction methods and ML models.

Provides off-the-shelf methods for common sensor data (e.g., Raw Data, Flatten, Image, Spectrogram, Audio MFE/MFCC, IMU, HR/HRV features).

Enables users to design their own feature extraction methods.

Supports training of classification, regression, or anomaly detection models.

Allows creation of custom learning blocks.

Provides options to modify ML training code.

Supports testing models using a holdout set or live device data.

Deploys full impulses in various formats (C++ library, Linux process, Docker container, WebAssembly, pre-built firmware).

Automatically tries various impulse configurations to determine the best combination for on-device performance.

All aspects of the Studio can be scripted using a web API for MLOps pipelines.

For enterprise clients, easily monitor and maintain projects.

Configure and run transformation blocks to extract, transform, and load (ETL) data.

Helps determine which features are most significant for analysis.

Visualizes raw dataset data.

Visualizes features generated by processing blocks to identify outliers and class separation.

State-of-the-art algorithms for detecting anomalies in all sensor modalities.

Specialized model for industrial object detection.

Understands and improves model performance in real-world scenarios.

Continuously enhances models with real-world data in production.

Tools to generate synthetic data for various data types.

Assess memory, flash, and latency requirements for specific devices.

Compiles code specifically for target devices.

Tools for optimizing model parameters for specific devices.