Annotating Data at Scale in Real Time

Real-time data annotation at petabyte scale challenges enterprises. A new architecture uses large language models (LLMs), feedback loops, and active learning to enhance efficiency and quality. LLMs automate initial annotations, with human reviewers refining results. Active learning prioritizes uncertain samples, reducing workload. Edge devices enable on-site, low-latency annotation for applications like autonomous driving. This approach combines automated and human-in-the-loop processes to handle massive datasets while maintaining accuracy.