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Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

A unified framework for machine learning with time series

Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.

A python library for user-friendly forecasting and anomaly detection on time series.

TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.

The collection of pre-trained, state-of-the-art AI models for ailia SDK

Anomaly Detection and Correlation library

ML powered analytics engine for outlier detection and root cause analysis.

Python module for hyperspectral image processing

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

Log Anomaly Detection - Machine learning to detect abnormal events logs

Streaming anomaly detection in Python: 16 online detectors (xStream, LODA, RS-Hash, Half-Space Trees, RRCF…) plus calibration and prequential evaluation for real-time data.

A distributed Spark/Scala implementation of the isolation forest and extended isolation forest algorithms for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

Local safety layer for AI agents that use the terminal. Screens risky commands and MCP/tool calls, watches Linux activity with eBPF, blocks dangerous behavior, and keeps audit trails local. Open source, self-hosted, dry-run by default.

Global maritime intelligence platform for real-time vessel tracking, AIS data analysis, sanctions monitoring, shipping routes, port activity, anomaly detection, OSINT analytics, and geospatial insights

Detection of rootkit file hiding activities through analysis of shifts in kernel function execution times.

Lightweight Docker monitoring dashboard with anomaly detection & Telegram alerts. One-liner install, zero config.

Self-hosted 4D spatiotemporal AI platform built on PostgreSQL, PostGIS, TimescaleDB, H3 and pgvector, with anomaly & threat detection, NLQ interface and full observability.

Declarative data quality engine. Define checks in YAML, run anywhere.

A detective for your data. Zero-config data quality monitoring — works with dbt, Postgres, BigQuery, Snowflake. No YAML.

Unified observability gateway for AI agents — one MCP server for Prometheus, Loki, and any backend, with cross-signal anomaly detection and a built-in Web UI.

Detects structural waste in cloud billing exports (AWS/GCP/Azure) using rolling-median + MAD, robust to the masking effect that breaks mean+stddev anomaly detection. Python library, CLI, FastAPI service, and GitHub Action.

Python SDK for WaveGuard physics-based anomaly detection API. One call. Any data.

Multi-cloud cost management MCP server (Azure + GCP). Read-only access to spending, anomalies, forecasts, budgets, and guided FinOps workflows. Ask your AI about your cloud bill.