MUSE: Multi-Tenant Model Serving With Seamless Model Updates

TL;DR

MUSE is a scalable model serving framework that allows seamless updates and stable decision thresholds in multi-tenant environments, significantly reducing lead times and operational costs.

Contribution

MUSE introduces a novel decoupling of model scores from decision boundaries, enabling dynamic, intent-based routing and stable score transformations for multi-tenant model deployment.

Findings

Processes over 55 billion events annually

Reduces model update lead time from weeks to minutes

Maintains high-availability and low-latency at scale

Abstract

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.