ClosedLoop’s ML Ops enables data scientists to seamlessly deploy models and drive success at scale. To support continuous improvement and learning in an ever-changing healthcare environment, ML Ops provides a suite of capabilities to not only configure and manage deployments, but automate model performance monitoring and effortlessly retrain models, with support for audit and governance.
ClosedLoop’s Enterprise Healthcare Feature Store (EHFS) provides all the features and functionality of traditional feature stores – enabling shareability, reusability, and consistency of features from model training to production and maintenance. Further, it enables data scientists to develop a centralized feature repository with unmatched efficiency through its Healthcare Feature Library.
ClosedLoop.ai, which placed first out of 300+ teams in the CMS AI Health Outcomes Challenge, has AutoML capabilities that are purpose-built to handle messy healthcare data and streamline model development from preprocessing to validation. Built-in model explainability reports leverage patent-pending Factor EvidenceTM technology and cover everything from population-level statistics to individual patient histories.