Model development with MLOps, monitoring, and bias testing.
Machine Learning
- The Challenge Building an ML model is just the first step. Without proper operational practices (MLOps), models can degrade in production, exhibit unfair bias, and become a 'black box' that no one understands or trusts.
- Our Approach We provide end-to-end services for responsible AI. This includes model development, robust MLOps for managing the entire model lifecycle, continuous monitoring for performance drift, and rigorous testing to detect and mitigate bias.
- Our Experience To help care managers, we developed risk stratification models that accurately predicted patient needs. These models were integrated with a rules engine to help prioritize outreach and other care management activities.
- The Outcomes Deploy fair, reliable, and high-performing machine learning models. Automate complex tasks, make more accurate predictions, and make decisions with confidence while managing risk effectively.
