Your ML team is wasting 60% of its time on operational overhead.

CorePlexML is a single platform that handles the entire machine learning lifecycle — from preparing your data to deploying predictive models in production and monitoring them in real time. It replaces the 6-7 separate open-source tools your team currently has to install, integrate, and maintain.

Most ML teams already use open-source tools — MLflow for experiment tracking, Seldon or a custom Flask service for deployment, Evidently for monitoring, Great Expectations for data validation. The tools themselves are free. The real cost is the engineering time to make them all work together. Someone has to deploy MLflow on a server, write the integration between your training pipeline and the model registry, build monitoring dashboards, configure alerting, set up data validation pipelines, and handle privacy compliance manually. That quietly absorbs 80–100 hours per month of senior engineering time — roughly half to a full-time engineer — that never goes toward building actual models.

CorePlexML eliminates that overhead. Everything is pre-integrated and works out of the box — training, deployment, monitoring, privacy, explainability, and data preparation. Your MLOps engineer stops maintaining infrastructure and starts improving models. Your ML engineers stop context-switching between 6 different tools and run more experiments, test more hypotheses, and iterate faster. The result: your organization ships production AI sooner and with higher confidence.