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LLMs can now collaboratively pinpoint root causes in microservices using a tree-structured search, but production environments reveal the limitations of this approach when faced with polyglot stacks and inconsistent logging.
Quantizing BERT and training it across multiple systems lets you achieve anomaly detection performance on par with full BERT, but with the speed of static word embeddings.
