NannyML is an open-source Python library and cloud platform for MLOps professionals to estimate post-deployment model performance and detect data drift and concept drift without needing access to targets.
NannyML provides a crucial solution for monitoring machine learning models after they have been deployed into production, addressing the problem of "silent model failure" where performance degrades over time. Primarily serving data scientists, ML engineers, and MLOps teams, its core value is its ability to estimate model performance without requiring ground truth labels, which are often delayed or unavailable. The platform uniquely offers both a powerful open-source Python library for code-based monitoring and a managed cloud solution for scalable, collaborative observability. This dual-offering approach caters to individual practitioners building custom workflows and larger organizations seeking robust, enterprise-grade ML monitoring. By focusing on performance estimation and drift, it helps teams proactively maintain model health and business value.
Data Scientists, Machine Learning Engineers, and MLOps teams responsible for deploying, monitoring, and maintaining the performance of machine learning models in production environments.
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2020
Ghent, Belgium
Open Source
The core Python library. Includes performance estimation (CBPE) and drift detection. Self-hosted and community-supported.
Free
Cloud Free
Managed cloud platform. Monitor up to 2 models, includes 1-day data retention, UI & dashboards, and email alerts.
Free
Cloud Pro
For growing teams. Monitor 3+ models, offers 30-day data retention, advanced alerting (Slack, Pagerduty), and priority support.
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Cloud Enterprise
For large-scale deployments. Custom number of models, configurable data retention, user management with SSO, and a dedicated success manager.
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An open-source alternative often chosen for its comprehensive, pre-built visual reports and integrated data/model testing capabilities.
A broad, enterprise-grade MLOps observability platform that provides more extensive troubleshooting and root-cause analysis features beyond just drift detection.
A competitor focused on model governance and explainable AI (XAI), making it a strong choice for regulated industries needing deep model insights and bias detection.
An observability platform known for its efficient data logging and profiling capabilities, with strong support for both ML models and unstructured data like in LLMs.
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