NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift,

💡 What is NannyML?

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2022-05-15 09:00:05

NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, interactive visualizations, is completely model-agnostic and currently supports all tabular binary classification use cases.

The core contributors of NannyML have researched and developed a novel algorithm for estimating model performance: confidence-based performance estimation (CBPE). The nansters also invented a new approach to detect multivariate data drift using PCA-based data reconstruction.

If you like what we are working on, be sure to become an Nanster yourself, join our community slack and champion us with a GitHub star ⭐.

NannyML closes the loop with performance monitoring and post deployment data science, empowering data scientist to quickly understand and automatically detect silent model failure. By using NannyML, data scientists can finally maintain complete visibility and trust in their deployed machine learning models. Allowing you to have the following benefits:

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