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.
Download the product brief to learn more about ClosedLoop's ML Ops and why it matters for AI-enabled healthcare organizations.
Deploying and managing machine learning models for use in operational or clinical workflows is no easy task. Approximately 90% of models never make it into production, and successfully maintaining the 10% that do requires comprehensive ML Ops capabilities. ML Ops enhances the DevOps procedures used to deploy traditional software with the unique requirements of ML systems. For models to be integrated into workflows and drive improved health and financial outcomes, data scientists must integrate with standard IT DevOps procedures while also handling enormous volumes and varieties of data from diverse sources, monitoring model performance, reacting to degradation in model accuracy, and maintaining robust governance and change management processes.
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