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[2602.11776] MUSE: Multi-Tenant Model Serving With Seamless Model Updates
Summary
The paper presents MUSE, a framework for multi-tenant model serving that allows seamless updates of machine learning models, optimizing decision thresholds and reducing operational costs.
Why It Matters
MUSE addresses the challenges of model recalibration in multi-tenant environments, which is crucial for businesses relying on accurate predictions. By decoupling model scores from client-specific decision boundaries, it enhances model resilience and operational efficiency, significantly impacting industries like finance and fraud detection.
Key Takeaways
- MUSE enables seamless updates of machine learning models in multi-tenant environments.
- It decouples model scores from client decision thresholds, facilitating easier recalibration.
- The framework has been deployed at scale, processing over 55 billion events in the past year.
- MUSE reduces model lead time from weeks to minutes, enhancing operational efficiency.
- It helps save millions in fraud losses and operational costs.
Computer Science > Machine Learning arXiv:2602.11776 (cs) [Submitted on 12 Feb 2026 (v1), last revised 24 Feb 2026 (this version, v2)] Title:MUSE: Multi-Tenant Model Serving With Seamless Model Updates Authors:Cláudio Correia, Alberto E. A. Ferreira, Lucas Martins, Miguel P. Bento, Sofia Guerreiro, Ricardo Ribeiro Pereira, Ana Sofia Gomes, Jacopo Bono, Hugo Ferreira, Pedro Bizarro View a PDF of the paper titled MUSE: Multi-Tenant Model Serving With Seamless Model Updates, by Cl\'audio Correia and 9 other authors View PDF HTML (experimental) 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-le...