An in-database machine learning solution to run python models in Postgres

The codebase for DBSim

Demo of an In-database processing tool for scikit-learn

Tools to create database-specific text value embeddings from word embedding datasets

Caret R Models Deployment using SQL databases

Loading, accessing and visualizing data from Netezza Performance server

Data analytics and prediction using Netezza Performance Server

GLM regression (logistic, linear, Poisson, Gamma, Tweedie, negative binomial, multinomial) in pure DuckDB SQL — fit, predict & evaluate as table macros, with ridge, offset/exposure & weights

Linear Regression Benchmark Workflows repository For the VLDB 2018 Research Paper "AIDA - Abstraction for Advanced In-Database Analytics"

Master's thesis implementation of SQLSIM: executing similarity queries and clustering directly in PostgreSQL to enable in-database analytics.

Apache MADlib is an open-source library for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical, and machine learning methods for structured and unstructured data, executed within PostgreSQL or Greenplum Database.

Random forests (classification & regression) as pure DuckDB SQL macros — no extensions, no driver. Sibling of duckLM.