Automl
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A curated list of awesome open-source libraries for deploying, monitoring, versioning, and scaling production machine learning systems.
A hyperparameter optimization framework for machine learning with a define-by-run API for dynamic search spaces.
A low-code declarative framework for building custom LLMs, neural networks, and other AI models with YAML configurations.
A low-code declarative framework for building custom LLMs, neural networks, and other AI models with YAML configurations.
An automated machine learning library that trains and deploys high-accuracy models for tabular, text, image, and time series data with minimal code.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
An AutoML library for deep learning that automates model selection and hyperparameter tuning using Keras and TensorFlow.
An automated machine learning toolkit that serves as a drop-in replacement for scikit-learn estimators.
An open-source Python library for automated feature engineering using Deep Feature Synthesis.
An open-source Python library for automated feature engineering using Deep Feature Synthesis.
An open-source, in-memory platform for distributed and scalable machine learning with support for a wide range of algorithms and big data technologies.
A collection of samples demonstrating how to use ML.NET for various machine learning tasks in .NET applications.
A PyTorch framework for deep learning research and development, focusing on reproducibility and rapid experimentation.
An Automated Machine Learning Python package for tabular data with feature engineering, hyperparameter tuning, explanations, and automatic documentation.
An easy-to-use, scalable hyperparameter optimization framework for Keras models with define-by-run syntax and built-in search algorithms.
Automatic neural architecture search and hyperparameter optimization for PyTorch, focusing on tabular data and time series forecasting.
Automatically visualize any dataset with a single line of code, including data quality assessment and fixes.
A unified framework for implementing and training deep learning models on tabular data using PyTorch and PyTorch Lightning.
Automated machine learning library for production and analytics, handling feature engineering, model selection, and hyperparameter optimization.
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