As AI rapidly moves to the edge and demand for smart edge devices skyrockets, developers are expected to act faster. However, they struggle to adapt powerful models to tiny microcontrollers and face a steep learning curve. Recognizing this need, ADI has co-developed AutoML for Embedded to make edge AI accessible, efficient, and scalable for everyone.
AutoML for Embedded simplifies the process by automating the machine learning process from start to finish, enabling developers without data science expertise to create efficient, high-quality models that deliver robust performance. In a recent demonstration, the tool was used to create an anomaly detection model for time-series sensor data on the ADI MAX32690 MCU. The model was deployed to both physical hardware and its digital twin in the Renode simulation, demonstrating seamless integration and real-time performance monitoring.
