NannyML — AI for Software Development

For software developers working with machine learning, the post-deployment phase is critical and often opaque. Models can fail silently due to data drift or concept drift, leading to poor business outcomes. NannyML directly addresses this by providing tools to estimate model performance in real-time, even without immediate access to target data. Its key capability is its performance estimation algorithms (like Confidence-based Performance Monitoring and Direct Loss Estimation), which calculate likely performance metrics (AUC, F1 score, etc.) based on the input data and model outputs alone. This allows you to set up alerts and dashboards to catch issues early. Because it's an open-source Python library, it integrates directly into your existing MLOps pipelines (using tools like Airflow or Kubeflow) and works with any model, regardless of the framework (Scikit-learn, TensorFlow, PyTorch). The optional cloud dashboard provides a visual interface for monitoring and collaboration, turning raw monitoring data into actionable insights for the entire development team.