Likliide

Likliide is an open-source, modular analytics framework designed to estimate and explain the likelihood of events in complex data systems. It emphasizes probabilistic modeling and privacy-conscious data processing.

The framework is built to operate on both streaming and batch data, combining probabilistic reasoning, transparent outputs, and interpretable dashboards. It supports Bayesian and frequentist approaches and aims to provide calibrated probability estimates.

Core components include a data ingestion layer, a feature store, a probabilistic modeling engine, a decision engine, and a presentation layer. It is designed to support on-premises, cloud, and edge deployments to accommodate diverse data environments.

Key features encompass a plugin-based architecture, a model zoo with pre-trained priors, automatic calibration, uncertainty quantification, explanations for outputs, audits and lineage tracking, and privacy-preserving options such as differential privacy and on-device inference.

Likliide was developed by a community of researchers and engineers to be language-agnostic and interoperable with standard data formats. It has seen pilots and early adoption across sectors such as commerce, cybersecurity, and IoT, and maintains an active open-source repository with regular plugin releases.

Common use cases include predicting purchase likelihood, detecting fraud signals, forecasting equipment failures, estimating churn risk, and guiding decision-making under uncertainty in operations and strategy.

Governance is designed to be open and collaborative, with contributions routed through an issue-tracker system and a permissive licensing model intended to maximize interoperability and community participation.

See also: likelihood, probabilistic programming, Bayesian inference, differential privacy.