"On-device model fine-tuning for industrial anomaly detection applications"
Konstantin Meshcheriakov
Solution Architect
Klika Tech
Lifelong machine learning is an advanced paradigm for improving the performance of anomaly detection in changing conditions of industrial environments. With on-device fine-tuning, pre-trained neural networks can adapt to new data. Efficient on-device learning can be done with a small memory footprint allowing models to run inference and continuously fine-tune newly collected data.
Join this talk to learn more about improving the flexibility of ML models and avoiding issues connected with continuous training, such as catastrophic forgetting. This presentation will document the process of moving an AWS cloud-based anomaly detection application to an MCU in the same time decreasing infrastructure costs and simplifying the operational efforts.
