Research Paper#AI Model Deployment, Optimization, Cost-Benefit Analysis🔬 ResearchAnalyzed: Jan 3, 2026 18:44
ML Compass: Optimizing AI Model Deployment with Trade-offs
Published:Dec 29, 2025 14:19
•1 min read
•ArXiv
Analysis
This paper addresses a critical problem in AI deployment: the gap between model capabilities and practical deployment considerations (cost, compliance, user utility). It proposes a framework, ML Compass, to bridge this gap by considering a systems-level view and treating model selection as constrained optimization. The framework's novelty lies in its ability to incorporate various factors and provide deployment-aware recommendations, which is crucial for real-world applications. The case studies further validate the framework's practical value.
Key Takeaways
- •Addresses the capability-deployment gap in AI model selection.
- •Proposes ML Compass, a framework for constrained optimization of model choice.
- •Considers user utility, deployment costs, and compliance requirements.
- •Provides deployment-aware recommendations that differ from capability-only rankings.
- •Validates the framework with case studies in conversational and healthcare settings.
Reference
“ML Compass produces recommendations -- and deployment-aware leaderboards based on predicted deployment value under constraints -- that can differ materially from capability-only rankings, and clarifies how trade-offs between capability, cost, and safety shape optimal model choice.”