Design a high-throughput, low-latency machine learning system to automatically classify millions of multi-modal documents into a complex, hierarchical taxonomy with over 1,000 labels. The system must handle long-form text, provide calibrated confidence scores for automated downstream processing, and include a strategy for handling label drift and human-in-the-loop feedback.