Loading...
Loading...
RxInfer.Jl: Julia package for automated Bayesian inference on a factor graph with reactive message passing. It is listed under Data & Analytics on AI Registry as a free tool.
The tools below are approved listings in the same category, ordered by how closely their tags match RxInfer.Jl. This is an automatic list. We have not written a researched verdict for RxInfer.Jl yet, so treat it as a shortlist and check each vendor's pricing page before switching.
Built from each tool's listing. Vendors can update their own listing after claiming it.
Deep universal probabilistic programming with Python and PyTorch
Pyro is a universal probabilistic programming language written in Python and supported by PyTorch on the backend. It is used for deep probabilistic modeling, combining modern deep learning with Bayesian methods so that users can describe a model as code and run inference over it. The project lists four design principl...
An open-source toolkit for large-scale genomic analysis
Glow is an open source toolkit for large scale genomic analysis, built natively on Apache Spark. It lets researchers aggregate genomic data and run data preparation, statistical analysis and machine learning at biobank scale and beyond, using the same Spark SQL engine that many organizations already use for petabyte cl...
AI powered open source recommender system engine supports classical/LLM rankers and multimodal content via embedding
Gorse is an open source recommender system engine written in Go. You import items, users and interaction data, and the system automatically trains models to generate recommendations for each user. It aims to be a universal recommender that can be added to a wide range of online services, such as content sites, shops an...
LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence https://arxiv.org/abs/2609.17488
LimiX is a family of foundation models for structured data, meaning rows and columns of tabular data. The current release, LimiX 2, is built around a design the developers call Contextual Mechanism Networks. Instead of training a separate model for each dataset, a single pretrained model reads a table as context and ca...
MOA is an open source framework for Big Data stream mining.
MOA, short for Massive Online Analysis, is an open source Java framework for machine learning on data streams, developed at the University of Waikato and related to the WEKA project. It is built for settings where data arrives continuously and models must learn incrementally, and a book about it has been published by M...
nannyml: post-deployment data science in python
NannyML is a post deployment monitoring product for machine learning models. Its core idea is to estimate model performance when ground truth labels are delayed or missing, so a team can see how a model is doing and be alerted when performance drops rather than when data merely drifts. The company says this cuts false ...