Explore on-device model fine-tuning for industrial anomaly detection applications in this 59-minute tinyML Talk. Delve into lifelong machine learning paradigms for improving anomaly detection performance in changing industrial environments. Learn how pre-trained neural networks can adapt to new data through on-device fine-tuning with a small memory footprint, allowing continuous model improvement during inference. Discover strategies for enhancing ML model flexibility while addressing challenges like catastrophic forgetting. Follow the process of transitioning an AWS cloud-based anomaly detection application to a microcontroller unit (MCU), reducing infrastructure costs and simplifying operational efforts. Gain insights into maintenance types, cloud architecture references, robust random cut techniques, and practical demonstrations showcasing the benefits of on-device learning for industrial applications.