Cortex AI Labs

Data scientists built models in notebooks that never reached production. No CI/CD for ML, no model monitoring, no feature store. Each deployment was a manual, fragile process. ROI on ML investment was invisible.

Built an end-to-end MLOps platform with automated pipelines, a feature store, model monitoring, and a deployment system that turned notebooks into production APIs in minutes.

Model deployment time dropped from weeks to hours, 5x more models reached production, and the team measured real business impact per model. ML investment finally had clear ROI.

We built an end-to-end MLOps platform with automated training pipelines triggered by data changes. A centralized feature store eliminated duplicate work and ensured consistency between training and serving. Model monitoring tracked drift, performance, and business impact in real time. A deployment system turned any notebook into a versioned, monitored production API in hours.