Analog Devices Inc. (ADI) has introduced AutoML for Embedded, an AI tool that automates the end-to-end machine learning pipeline for edge AI. The tool, co-developed with Antmicro, is now available as part of the Kenning framework, integrated into CodeFusion Studio. The Kenning framework is a hardware-agnostic and open-source platform for optimizing, benchmarking, and deploying AI models on edge devices. AutoML for Embedded allows developers without data science expertise to build high-quality and efficient models that deliver robust performance. The tool automates model search and optimization using state-of-the-art algorithms, leveraging SMAC to explore model architectures and training parameters efficiently. It also verifies model size against the device’s RAM to enable successful deployment. Candidate models can be optimized, evaluated, and benchmarked using Kenning’s standard flows, with detailed reports on size, speed, and accuracy to guide deployment decisions. Antmicro’s Michael Gielda, VP Business Development, said that AutoML in Kenning reduces the complexity of building optimized edge AI models, allowing customers to take full control of their products. AutoML for Embedded is a Visual Studio Code plugin built on the Kenning library that supports: ADI MAX78002 AI accelerator MCUs and MAX32690 devices — deploy models directly to industry-leading edge AI hardware. Simulation and RTOS workflows — leverage Renode-based simulation and Zephyr RTOS for rapid prototyping and testing. General-purpose, open-source tools — allowing flexible model optimisation without platform lock-in