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The paper introduces MUSE, a multi-tenant model serving framework designed to address the challenge of threshold recalibration in Score-as-a-Service environments caused by model updates. MUSE decouples model scores from client decision boundaries using dynamic intent-based routing and a two-level score transformation to map model outputs to a stable reference distribution. Deployed at Feedzai, MUSE significantly reduces model lead time from weeks to minutes, processing over a thousand events per second across dozens of tenants, leading to substantial savings in fraud losses and operational costs.
Stop manually recalibrating thresholds after every model update: MUSE enables seamless model updates in multi-tenant environments by decoupling model scores from client decision boundaries.
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.