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The scale of the global edge AI market continues to grow. The currenttechnical challenges that hinder the large-scale replication of edge AI aremainly small samples on the edge and heterogeneity of edge data. In addition,edge AI customers often have requirements for data security compliance andoffline autonomy of edge AI services. Based on the lifelong learning method inthe academic world, we formally define the problem of edge-cloud collaborativelifelong learning for the first time, and release the industry's firstopen-source edge-cloud collaborative lifelong learning. Edge-cloudcollaborative lifelong learning adapts to data heterogeneity at different edgelocations through (1) multi-task transfer learning to achieve accurateprediction of "thousands of people and thousands of faces"; (2) incrementalprocessing of unknown tasks, the more systems learn and the smarter systems arewith small samples, gradually realize AI engineering and automation; (3) Usethe cloud-side knowledge base to remember new situational knowledge to avoidcatastrophic forgetting; (4) The edge-cloud collaborative architecture enablesdata security compliance and edge AI services to be offline autonomy whileapplying cloud resources. This work hopes to help fundamentally solve theabove-mentioned challenges of edge-cloud collaborative machine learning.

KubeEdge-Sedna v0.3: Towards Next-Generation Automatically Customized AI Engineering Scheme