KubeEdge-Sedna v0.3: Towards Next-Generation Automatically Customized AI Engineering Scheme
This work addresses the problem of scalable and compliant edge AI deployment for industry applications, representing an incremental advancement by adapting academic lifelong learning methods to a practical edge-cloud setting.
The paper tackles the challenges of small samples and data heterogeneity in edge AI by proposing an edge-cloud collaborative lifelong learning framework, which achieves accurate prediction through multi-task transfer learning and incremental processing while ensuring data security and offline autonomy.
The scale of the global edge AI market continues to grow. The current technical challenges that hinder the large-scale replication of edge AI are mainly small samples on the edge and heterogeneity of edge data. In addition, edge AI customers often have requirements for data security compliance and offline autonomy of edge AI services. Based on the lifelong learning method in the academic world, we formally define the problem of edge-cloud collaborative lifelong learning for the first time, and release the industry's first open-source edge-cloud collaborative lifelong learning. Edge-cloud collaborative lifelong learning adapts to data heterogeneity at different edge locations through (1) multi-task transfer learning to achieve accurate prediction of "thousands of people and thousands of faces"; (2) incremental processing of unknown tasks, the more systems learn and the smarter systems are with small samples, gradually realize AI engineering and automation; (3) Use the cloud-side knowledge base to remember new situational knowledge to avoid catastrophic forgetting; (4) The edge-cloud collaborative architecture enables data security compliance and edge AI services to be offline autonomy while applying cloud resources. This work hopes to help fundamentally solve the above-mentioned challenges of edge-cloud collaborative machine learning.