Automl
Showing 33 of 33 projects
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 open-source, low-code Python library that automates end-to-end machine learning workflows.
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.
A versatile Bayesian optimization package for hyperparameter optimization of machine learning algorithms.
An autoML framework and toolkit for automating machine learning tasks on graph-structured data.
A unified Python interface for constructing and managing workflows across engines like Argo Workflows, Tekton Pipelines, and Apache Airflow.
A hyperparameter-free gradient boosting machine with a simple budget parameter, built for high performance with Rust and bindings for Python and R.
A Ruby library for building and serving predictive models with support for PMML and integration with Python and R models.
An AutoML implementation and tutorial for automating machine learning pipelines on both static datasets and dynamic data streams, with a focus on IoT anomaly detection.
Automatically builds high-performance interpretable machine learning models with minimal features using a single line of code.
An AutoML framework that generates and customizes machine learning pipelines using declarative JSON-AI syntax.
A Python library for fast, reproducible, and modular Neural Architecture Search (NAS) to generate efficient deep networks.
A curated list of research, applications, tutorials, and software built using the H2O open-source machine learning platform.
An intelligent data search and enrichment library for machine learning that automatically finds and adds relevant external features to ML pipelines.
A decentralized hyperparameter optimization framework for Go, inspired by Optuna, supporting Bayesian optimization and evolution strategies.
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