Edge Impulse is a platform focused on deploying ML models to low-power edge devices and embedded systems. It supports both vision and other models like audio, time-series, and signal processing. Edge Impulse is uniquely good at working withmicrocontrollers and has SDKs for single-board computers and mobile devices.
The design focus on TinyML makes it less suited for high-resource, general-purpose tasks like video processing and running modern, state-of-the-art ML models. It also requires some familiarity with embedded systems. It typically requires custom coding your application logic to run on the embedded board.
Chose Edge Impulse if: you're working on an IoT or wearable device that's not capable of running more powerful models, framework, and logic.
The PyTorch ecosystem's equivalent of Tensorflow Serving is TorchServe. It's optimized for serving PyTorch models across several domains including vision, NLP, tabular data, and audio.
Like Tensorflow Serving, it is designed for large-scale cloud deployments and can require custom configuration for things like pre- and post-processing and deploying multiple models. Because of its wide mandate it lacks many vision-specific features (like video streaming).
Chose TorchServe if: you're looking for a way to scale and customize the deployment of your PyTorch models and don't need vision-specific functionality.
Inference turns any computer or edge device into a command center for your computer vision projects.
