What is NannyML?
NannyML is an open-source python library for estimating post-deployment model performance (without access to targets), detecting 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 use cases, classification and regression.
➖ Performance Estimation and Calculation
NannyML tracks to monitor the model’s performance even if the ground truth is delayed or absent.
Using performance estimation algorithms, users can track any metric like, i.e., accuracy, ROC AUC, or MSE and RMSE.
This enables real-time monitoring and quick reaction when the model is degrading.
➖ Business Value Estimation and Calculation
NannyML uses business value metric estimation using the CBPE algorithm, which provides a way to tie the performance of a model to business-oriented outcomes.
This allows user to monitor and better understand the model’s monetary value.
➖ Data Quality
NannyML supports testing users’ data quality. It allows checking for missing and unseen values in categorical columns.
These changes are plotted over time to help users better understand the data.
➖ Multivariate Drift Detection
To detect multivariate feature drift, NannyML uses PCA-based data reconstruction method which detects subtle changes in the data structure that cannot be detected with univariate drift detection methods.
These changes are monitored over time and data drift alerts are triggered when the reconstruction error exceeds a threshold.
➖ Univariate Drift Detection
NannyML supports six methods to detect univariate feature drift in both categorical and continuous features.
Generated results by those methods are tracked over time, allowing users to correlate them with changes in the model’s performance and take appropriate actions.
➖ Custom Thresholds
When the model’s performance exceeds the upper threshold or drops below the lower threshold, NannyML will flag that value as an alert.
To offer users maximum flexibility, NannyML incorporates two threshold options: a constant value threshold and a standard deviation-based threshold.
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