India, July 17 -- AI is rapidly moving to the edge with demand for intelligent edge devices exploding, but many developers still struggle to fit powerful models onto tiny microcontrollers. Developers face a steep learning curve, as they juggle data preprocessing, model selection, hyperparameter tuning and hardware-specific optimizations. Now imagine building and deploying robust, resource-intensive machine learning models on edge devices like microcontrollers and other constrained platforms, without wrestling with complex code or hardware constraints.

We are thrilled to introduce AutoML for Embedded, co-developed by Analog Devices, Inc. (ADI) and Antmicro, now available as part of the Kenning framework, a hardware-agnostic and open-sourc...