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Graph Data Science Client: A Python client for the Neo4j Graph Data Science (GDS) library. 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 Graph Data Science Client. This is an automatic list. We have not written a researched verdict for Graph Data Science Client 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.
Hopsworks - Data-Intensive AI platform with a Feature Store
Hopsworks is an AI lakehouse and machine learning platform built around a feature store. It covers the full ML lifecycle in one system: a central repository for feature data with sub millisecond online retrieval, a model registry, experiment tracking, model serving and deployment pipelines, and monitoring. The feature...
Interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible).
imodels is an open source Python package for concise, transparent and accurate predictive modeling. It gives data scientists a scikit learn compatible interface to interpretable models, so a classifier or regressor can be imported, fitted and used with the familiar fit and predict methods. The aim is to replace black b...
Build, run, and manage data pipelines for integrating and transforming data.
Mage AI is an AI data engineering platform for running production data pipelines. It describes itself as putting pipelines on autopilot: it checks data quality, diagnoses and recovers from failures, and optimizes performance as data grows. Users can describe what they need and have AI create a scheduled pipeline with ...
Machine Learning in R
The mlr package is a machine learning framework for the R programming language. It provides a single interface to a large number of classification, regression, survival analysis and clustering methods, so users do not need to write separate wrappers for each algorithm in R. Around that interface it supplies the surrou...
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 ...
The backend agents build with - Multimodal database, orchestration, and serving in one file
Pixeltable is a unified multimodal backend that combines a database, orchestration and serving in a single Python file. It is designed for applications that process images, video, audio and documents alongside ordinary columns, and for the agents and AI pipelines built on top of that data. A developer declares tables ...