Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs

Simple Transparent End-To-End Automated Machine Learning Pipeline for Supervised Learning in Tabular Binary Classification Data

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

The Cerebros package is an ultra-precise Neural Architecture Search (NAS) / AutoML that is intended to much more closely mimic biological neurons than conventional neural network architecture strategies.

TSForecasting: Automated Time Series Forecasting Framework

Library for streaming data and incremental learning algorithms.

Benchmark pipeline for evaluating language models on financial tasks, including sentiment analysis and credit scoring. Supports over ten tasks with modular design for easy integration of new tasks. Provides automated performance metrics for standardized evaluation, benefiting researchers and practitioners in finance.

Atlantic: Automated Data Preprocessing Framework for Supervised Machine Learning

Auto torch image models: train and evaluation

Sugar candy for data scientist. Easy manipulation in time-series data analytics works.

TinyAutoML is a comprehensive Pipeline Classifier Project thought as a Scikit-learn plugin

Powerful AutoML toolkit

A GitHub compiling the input data, Python and Jupyter Notebook scripts, and all relevant statistical outputs from running the AutoMLPipe-BC automated machine learning pipeline (from the Urbanowicz Lab - https://github.com/UrbsLab) on a large-scale single nucleotide polymorphism (SNP) dataset from patients with congenital heart disease (CHD)

This library aims at providing tools for an automatic machine learning approach. As many tools already exist to establish one or the other component of an AutoML approach, the idea of this library is to provide a structure rather than to implement a complete service.

This project aims to create Machine Learning models using Azure's AutoML to find the best model that fits the data and Hypderdrive to find the best hyperparameters.

AutoML as a Service.

Knowledge-driven AutoML

Shrinkit is a powerful GUI-based Python library designed for automating machine learning tasks. With its intuitive interface, Shrinkit simplifies the process of building, training, and evaluating machine learning models, making it accessible to users of all skill levels. Shrinkit is a No-code package which can be used as a GUI.

AutoML

Utilizes pycaret to automates machine learning workflows (Deployed at streamlit)