Alibi Detect
A Python library for outlier, adversarial, and drift detection in machine learning models, supporting tabular, text, image, and time series data.
What is Alibi Detect?
Alibi Detect is a Python library that provides algorithms for detecting outliers, adversarial attacks, and data drift in machine learning models. It helps maintain model reliability in production by monitoring data distributions and identifying anomalies across tabular, text, image, and time series data. The library supports both online and offline detection with flexible backends like TensorFlow and PyTorch.
Machine learning engineers and data scientists who need to monitor and maintain models in production environments, particularly those working on robust ML systems, model deployment, and data quality assurance.
Developers choose Alibi Detect for its comprehensive, multi-modal detection algorithms, support for popular ML frameworks, and principled approach to model monitoring. Its integration with tools like Seldon Core and KFServing makes it a practical choice for production ML pipelines.
Overview
Algorithms for outlier, adversarial and drift detection
Use Cases
Best For
- Monitoring machine learning models for data drift in production
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
