Machine LearningProduction MLMixture of Space ExpertsMoSEParameter-Efficient Fine-Tuning
From Static to Dynamic: Training Adaptive ML Models with Mixture of Space Experts for Production ML
9 min read · 1,622 wordsBy Orandi Felix
This pipeline runs on M-KOPA’s internal Kubernetes cluster. Each MoSE update job consumes only **~4GB GPU RAM** for subspace training—vs. 22GB needed for full XGBoost retraining.
For Ghana youth segment, MoSE adapter shifted approval rates by **1.42×**—achieving the regulatory target **without affecting unrelated customer segments**.
MoSE works best for **differentiable, parameter-rich models** (neural networks, XGBoost, CatBoost). It fails for **geometric, tree-based models** (Isolation Forest, HDBSCAN) and **ensemble rule learners**.