Alibi Detect is an open-source Python library developed by SeldonIO that focuses on monitoring, diagnosing, and responding to issues in deployed machine learning models. It's designed for data scientists, MLOps engineers, and research teams working in AI production environments who need to ensure the reliability, fairness, and accuracy of AI systems over time.

Alibi Detect provides tools for:

• Outlier detection (detecting anomalous data points)

• Concept drift and data drift detection (monitoring distribution shifts)

• Adversarial instance detection

• Pipeline health monitoring

Key benefits:

• Model-agnostic, working across frameworks like TensorFlow, PyTorch, or scikit-learn.

• Offers real-time monitoring capabilities to detect performance degradation as it happens.

• Integrates with Seldon Core, KFServing, and other deployment platforms.

What are the main features of Alibi Detect?

Outlier detection for structured and unstructured data

Alibi Detect includes multiple algorithms to detect individual anomalies in the input data or predictions of ML models:

• Supports tabular, image, and time-series data

• Algorithms include Variational Autoencoders (VAE), k-Nearest Neighbors (kNN), and autoencoder-based approaches

• Allows training on reference datasets and real-time evaluation of incoming data

• Helpful in identifying fraudulent entries, sensor failures, or data corruption

Data drift and concept drift detection

Drift detection tools identify changes in data distributions over time, which can silently reduce model performance:

• Data drift compares input distributions over time (e.g., via Kolmogorov-Smirnov tests, Maximum Mean Discrepancy)

• Concept drift tracks prediction output changes over time

• Drift detectors can be configured for batch or real-time modes

• Supports statistical significance thresholds and p-value monitoring

Adversarial detection modules

Alibi Detect includes functionality to spot inputs designed to fool ML models:

• Detects adversarial examples with statistical or learned models

• Suitable for image classification and other vision models

• Can be used in tandem with adversarial training workflows or robustness testing tools

Customizable model monitoring pipeline

The library enables building flexible pipelines for:

• Preprocessing and feature extraction with integrated tools (e.g., embeddings from pretrained models)

• Triggering alerts and responses when thresholds are crossed

• Exporting logs and metrics to Prometheus, Grafana, or cloud monitoring solutions

Integration with deployment tools and real-time inference

Alibi Detect is optimized for production environments:

• Easily integrates with Seldon Core, KFServing, BentoML, or custom APIs

• Supports on-demand or streaming evaluation

• Deployable as microservices for model monitoring across multiple endpoints

Why choose Alibi Detect?

• Flexible and framework-agnostic: Works with any ML model, supporting a wide variety of data types and ML frameworks.

• Supports production-scale deployment: Easily integrates with model serving tools and observability platforms.

• Comprehensive monitoring capabilities: Covers outliers, drift, and adversarial detection in one unified toolkit.

• Active open-source development: Maintained by SeldonIO with community support and ongoing updates.

• Transparent and customizable: Users can fine-tune detection thresholds, plug in custom models, or extend pipelines to meet domain-specific needs.