In binary classification systems, decision thresholds translate model scores into actions. Choosing suitable thresholds relies on the specific distribution of the underlying model scores but also on the specific business decisions of each client using that model. However, retraining models inevitably shifts score distributions, invalidating existing thresholds. In multi-tenant Score-as-a-Service environments, where decision boundaries reside in client-managed infrastructure, this creates a severe bottleneck: recalibration requires coordinating threshold updates across hundreds of clients, consuming excessive human hours and leading to model stagnation. We introduce MUSE, a model serving framework that enables seamless model updates by decoupling model scores from client decision boundaries. Designed for multi-tenancy, MUSE optimizes infrastructure re-use by sharing models via dynamic intent-based routing, combined with a two-level score transformation that maps model outputs to a stable, reference distribution. Deployed at scale by Feedzai, MUSE processes over a thousand events per second, and over 55 billion events in the last 12 months, across several dozens of tenants, while maintaining high-availability and low-latency guarantees. By reducing model lead time from weeks to minutes, MUSE promotes model resilience against shifting attacks, saving millions of dollars in fraud losses and operational costs.

翻译：在二分类系统中，决策阈值将模型分数转换为具体行动。选择合适的阈值不仅依赖于底层模型分数的特定分布，还取决于使用该模型的每个客户的具体业务决策。然而，模型重新训练不可避免地会导致分数分布发生变化，从而使现有阈值失效。在决策边界位于客户管理基础设施的多租户“分数即服务”环境中，这造成了严重的瓶颈：重新校准需要协调数百个客户的阈值更新，消耗大量人力时间并导致模型停滞。我们提出了MUSE，一种模型服务框架，通过将模型分数与客户决策边界解耦，实现无缝模型更新。MUSE专为多租户设计，通过基于动态意图的路由共享模型来优化基础设施复用，并结合两级分数变换，将模型输出映射到稳定的参考分布。MUSE已在Feedzai大规模部署，每秒处理超过一千个事件，在过去12个月内处理了超过550亿个事件，覆盖数十个租户，同时保持了高可用性和低延迟保证。通过将模型交付时间从数周缩短至数分钟，MUSE增强了模型应对动态攻击的韧性，节省了数百万美元的欺诈损失和运营成本。