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

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

TSForecasting: Automated Time Series Forecasting Framework

Atlantic: Automated Data Preprocessing Framework for Machine Learning

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.

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

Auto torch image models: train and evaluation

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

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.

Simplatab: An Automated & Explainable Machine Learning Framework

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 as a Service

Compose, train and test fast LLM routers

CI/CD Pipeline de MLOps diseñado para ser completamente autónomo extrayendo datos en vivo desde la API de Reddit, los procesa, entrena un modelo de ML propio con AutoML y despliega los resultados en un dashboard interactivo que se actualiza automáticamente.

Automating the ML Training Lifecycle with MLxOPS

Analyze the tunability of machine learning models with Grid Search, Random Search, and Bayesian Optimization. This project explores hyperparameter tuning methods on diverse datasets, comparing efficiency, stability, and performance. Featuring Random Forest, XGBoost, Elastic Net, and Gradient Boosting.

AutoML

Nano AutoML Cloud is a lightweight, and cloud-based ML pipeline for tabular data. It supports classification and regression, follows MLOps best practices, and is optimized for cloud deployment and CI/CD workflows.

Advanced CLI tool for automating Machine Learning (AutoML) using state-of-the-art deep learning models to apply transfer learning with multiple tuning methods and architecture modifications to pretrained models for image and text datasets, with end-to-end training for tabular and time series datasets.