Scaling ML Observability from Pilot to Portfolio: Statistical Methods and Production Architectures

Enterprise ML portfolios have evolved from isolated pilot projects to production systems spanning hundreds of models processing millions of daily predictions. This scale transformation fundamentally changes observability requirements—point-solution monitoring tools that sufficed for single-model deployments become untenable when organizations must track concept drift, data quality, and performance degradation across diverse model portfolios. This paper presents comprehensive architectures for s...