Qhzhou developed domain-weighted loss metrics for Autoencoder models in the pytorch/torchrec repository, focusing on expanding observability and enabling granular monitoring across domains. They introduced the serving_ae_loss metric, which tracks loss per domain using traffic weights, allowing for more data-driven optimization. Their approach involved Python-based metric instrumentation and comprehensive unit testing to ensure correctness and regression safety. Qhzhou collaborated with torchrec maintainers on the production-serving path, integrating the new metric into existing workflows. The work demonstrated depth in data processing and metric analysis, laying the groundwork for broader domain analytics and improved monitoring granularity without addressing major bug fixes.