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

Auto torch image models: train and evaluation

Automating the ML Training Lifecycle with MLxOPS

AutoML as a Service.

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

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

Library for streaming data and incremental learning algorithms.

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.

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.

AutoML

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.

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

TSForecasting: Automated Time Series Forecasting Framework

Atlantic: Automated Data Preprocessing Framework for Supervised Machine Learning