{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"Alibi Detect provides outlier, adversarial, and drift detection algorithms for tabular data, images, text, and time series, supporting both online and offline detection with TensorFlow and PyTorch backends.","skillfed_tags":["anomaly-detection","drift-monitoring","ml-ops"],"use_cases":["Detect outliers in tabular data using Isolation Forest or Mahalanobis Distance for real-time anomaly flagging.","Monitor for concept drift in production models by comparing training and live data distributions.","Identify adversarial examples in image classifiers using Adversarial AE or model distillation.","Detect anomalies in time series data with Seq2Seq or Prophet for sensor or financial data streams.","Flag unusual text inputs in NLP pipelines using model distillation\u2013based adversarial detection."],"what_it_does":"Alibi Detect is a detection library for identifying anomalies, distribution shifts, and adversarial examples in machine learning pipelines. It implements multiple algorithms\u2014Isolation Forest, VAE, Seq2Seq, Prophet, and others\u2014each suited to different data types (tabular, images, time series, text) and detection scenarios (online vs. offline). The library returns predictions as dictionaries containing metadata and detection scores, allowing you to flag suspicious instances or monitor for concept drift in production.\n\nThe package depends on standard scientific Python tools (numpy, pandas, scikit-learn, transformers) and optionally supports TensorFlow or PyTorch for neural network\u2013based detectors. It is designed for both research and production monitoring, though its Business Source License 1.1 requires careful review for commercial deployments.","worth_installing":"Yes, with conditions. Alibi Detect is well-maintained (active repository, recent release) and covers a broad range of detection tasks across data types. However, the Business Source License 1.1 is proprietary and may restrict commercial use\u2014verify licensing terms before deploying. The aging maintenance window (246 days) is not a blocker for stable use but suggests slower feature updates. Install if your use case aligns with the license and you need production-grade drift or outlier detection."},"id":"alibi-detect","links":{"html":"https://skillfed.io/packages/alibi-detect","md":"https://skillfed.io/packages/alibi-detect.md","pypi":"https://pypi.org/project/alibi-detect/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-12-11","license_spdx":null,"license_treatment":"unclear","name":"alibi-detect","python_support":"supports_current","summary":"Algorithms for outlier detection, concept drift and metrics."},"popularity":{"monthly_downloads":85379,"position":13926,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.13.0"}