NannyML NV

Deploying a machine learning model to production is only the first step. The real challenge begins afterwards: how do you know whether that model is still performing as it should? Data changes, user behaviour evolves, distributions shift — and a model that worked perfectly six months ago might today be making poor decisions without anyone noticing. NannyML was built to solve this specific problem: monitoring machine learning models after deployment. The platform enables data science teams to detect drift — the divergence between the data on which the model was trained and the data on which it operates today — and to estimate model performance even in the absence of immediate ground truth. This second capability is particularly important: in many real-world contexts, true labels arrive with significant delay or not at all, making it impossible to measure model performance using traditional methods. NannyML addresses this through proprietary statistical approaches that estimate performance degradation without requiring immediate feedback. The product is available as an open-source library and as a managed platform, allowing teams to start without dedicated infrastructure and scale according to their needs. The target audience is data science and MLOps teams at companies of all sizes that have models running in production and want to ensure their quality over time. In a rapidly maturing ML ecosystem, NannyML brings long-overdue attention to one of the most overlooked phases of the model lifecycle.