Julius AI
A conversational analysis service for working with tabular data, running statistical operations, and producing visualizations.
RESEARCH CATEGORY
Analyze data, run computations, and document workflows. The database currently includes 30 tools, models, databases, or infrastructure entries in this category.
A conversational analysis service for working with tabular data, running statistical operations, and producing visualizations.
A ChatGPT capability that runs code to inspect uploaded data, perform analyses, and produce tables or charts.
A technical computing system for symbolic mathematics, numerical methods, modeling, and visualization.
A programming and numerical-computing environment used for algorithm development, data analysis, simulation, and engineering models.
An open-source interactive computing environment that combines code, data, narrative, and visual output.
An analytics platform for assembling data access, transformation, modeling, and machine-learning workflows from visual nodes.
An open-source visual programming application for data exploration, machine learning, and teaching.
A collaborative data platform for preparation, machine learning, generative-AI projects, deployment, and governance.
An enterprise platform for automated machine learning, model deployment, monitoring, and AI governance.
A web-based analytics service that combines natural-language interaction with low-code analysis, prediction, and reporting.
An integrated development environment for R and selected Python workflows, available in open-source desktop and server editions and commercial variants.
An open-source data version control tool that records large datasets, models, and machine-learning pipelines alongside Git-managed code.
An open-source platform for recording machine-learning experiments, managing models and artifacts, and supporting self-hosted collaboration.
A hosted platform and client tools for recording, comparing, and collaboratively managing machine-learning experiments, models, and data artifacts.
Open-source Python infrastructure for running computational-science workflows and recording the provenance of their data and processes.
The foundational open-source Python library for multidimensional arrays, vectorized numerical operations, linear algebra, and related numerical routines.
An open-source Python library for reading, cleaning, transforming, joining, and summarizing tabular and time-series data.
An open-source scientific-computing library built on NumPy, with maintained algorithms for optimization, integration, signals, sparse computation, statistics, and more.
An open-source Python machine-learning library covering supervised and unsupervised estimators, preprocessing, pipelines, model selection, and evaluation.
An open-source probabilistic-programming library for specifying Bayesian models in Python and fitting them with MCMC or variational inference.
An open-source probabilistic-programming language and cross-language toolkit for Bayesian statistical modeling and posterior inference.
An open-source Python package for statistical modeling and econometrics, with model estimation, statistical tests, diagnostics, and structured result summaries.
An open-source DataFrame query engine written in Rust, with eager and lazy execution, query optimization, and streaming support.
An open-source in-process analytical SQL database for querying local files, object storage, and in-memory data without a separate database server.
An open-source Python library for high-performance array computing and composable transformations including automatic differentiation, compilation, batching, and parallelization.
An open-source framework for automated hyperparameter optimization with dynamic search spaces, trial pruning, parallel execution, and study visualization.
An open-source deep-learning library for tensor computation, automatic differentiation, neural networks, and distributed training on CPUs and supported accelerators.
An open-source framework for building, training, evaluating, and deploying machine-learning models, with related components for servers, browsers, and mobile devices.
An open-source Python framework for organizing modular, maintainable, and reproducible data-science code through pipelines, configuration, and a data catalog.
An open-source distributed data-management tool built on Git and git-annex for versioning, retrieving, and sharing research datasets of any size.