AutoML list

A compact and still under development list of open-source and commercial Automated Machine Learning (autoML) tools, such as libraries and systems.

Startup or tech giants AutoML tools appear in a separated list (Companies Tools, see below). If they offer open-source tools, they appear in both lists.

Name	Language	License*	AutoML Tasks**	Short Description	Source
AdaNet	Python	Apache 2.0	HPO/NAS	AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention	github
Advisor	Python	Apache 2.0	HPO	Advisor is the hyper parameters tuning system for black box optimization	github
ATM	Python	MIT	MS/MT	Auto Tune Models - A multi-tenant, multi-data system for automated machine learning	github
Auger A2ML	Python	Apache 2.0	HPO	The A2ML project is a Python API and set of command line tools to automate Automated Machine Learning tools from multiple vendors	link
AutoDL-Projects	Python	MIT	HPO/NAS	Automated Deep Learning Projects (AutoDL-Projects) is an open source, lightweight, but useful project for everyone	github
Auto-DL	Python/GUI	GPL-v3	Model Creation	Auto-DL is just DL but without code.	github
AutoFolio	Python	GPL-v2	HPO	AutoFolio uses algorithm configuration to optimize the performance of algorithm selection systems by determining the best selection approach and its hyperparameters.	bitbucket
AutoGL	Python	Apache 2.0	FE/MS/NAS/HPO/ME	An autoML framework & toolkit for machine learning on graphs	github
automl-gs	Python	MIT		automl-gs is an AutoML tool which offers a zero code/model definition interface to getting an optimized model and data transformation pipeline in multiple popular ML/DL frameworks	github
auto_ml	Python	MIT		Automated machine learning for analytics & production	github
auto-sklearn	Python	BSD-3-Clause	HPO/NAS	auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator	github
AutoGluon	Python	Apache 2.0	HPO/MS	AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning text, image, and tabular data.	github
Auto-Keras	Python	MIT	HPO/MS/NAS	AutoKeras: An AutoML system based on Keras	github
Auto-PyTorch	Python	Apache 2.0	HPO/MS/NAS	While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, another trend in AutoML is to focus on neural architecture search	github
AutoTS	Python	MIT	Time Series Forecast	AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale	github
Auto-ViML	Python	Apache 2.0	HPO/NAS	Automatically Build Variant Interpretable ML models fast	github
aw_nas	Python	MIT	NAS	aw_nas: A Modularized and Extensible NAS Framework	github
BayesianOptimization	Python	MIT	HPO	Pure Python implementation of bayesian global optimization with gaussian processes	github
claspfolio	Python	GPL-v2	HPO	claspfolio is a portfolio solver for ASP that makes use of machine-learning techniques for performing algorithm selection, choosing among different configurations of clasp	link
Compose	Python	BSD-3-clause		Compose is a machine learning tool for automated prediction engineering	github
Cooka	Python	Apache 2.0	HPO/MS/NAS	Cooka is a lightweight and visualization toolkit to manage datasets and design model learning experiments through web UI	github
DARTS	Python	MIT	Differentiable Architecture Search	DARTS: Differentiable Architecture Search	github
DeepTables	Python	Apache 2.0	HPO/MS/NAS	DDeepTables(DT) is a easy-to-use toolkit that enables deep learning to unleash great power on tabular data	github
determined	Python	Apache 2.0	HPO/MS	Determined: Deep Learning Training Platform	github
devol	Python	MIT		DEvol (DeepEvolution) is a basic proof of concept for genetic architecture search in Keras	github
EvalML	Python	BSD-3-clause		EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions	github
FAR-HO	Python	MIT		Gradient-based hyperparameter optimization and meta-learning package based on TensorFlow	github
Featuretools	Python	BSD-3-clause	FE	Featuretools is a python library for automated feature engineering	github
FLAML	Python	MIT	HPO	FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically	github
FlexFolio	Python	GPL-v2	HPO	flexfolio is a modular and open solver architecture that integrates several different portfolio-based algorithm selection approaches and techniques	bitbucket
GAMA	Python	Apache 2.0		Machine-learning pipeline optimization through asynchronous evaluation based genetic programming	github
Hugging Face	Python	Apache 2.0		"Build, train and deploy state of the art models powered by the reference open source in machine learning"	github
HyperBand	Python	BSD-2-clause		Code for tuning hyperparams with Hyperband, adapted from Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization	github
HpBandSter	Python	BSD-2-clause		a distributed Hyperband implementation on Steroids	github
HyperGBM	Python	Apache 2.0	HPO	A full pipeline AutoML tool integrated various GBM models	pypi
HyperKeras	Python	Apache 2.0	HPO/MS/NAS	An AutoDL tool based on Tensorflow and Keras	github
Hypernets	Python	Apache 2.0	HPO/MS/NAS	Hypernets: A General Automated Machine Learning Framework	github
Hyperopt	Python	Open Source	HPO	Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions	github
Hyperopt-sklearn	Python	Open Source	HPO	Hyperopt-sklearn is Hyperopt-based model selection among machine learning algorithms in scikit-learn	github
igel	Python	MIT	HPO/MS	A delightful machine learning tool that allows you to train/fit, test and use models without writing code	github
Kaggler	Python	MIT	HPO/MS	Kaggler is a Python package for lightweight online machine learning algorithms and utility functions for ETL and data analysis	github
KerasTuner	Python	Apache 2.0	HPO	KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search	github
Ludwig	Python	Apache 2.0	HPO	Ludwig is a data-centric deep learning framework that allows users to train and test deep learning models by specifying a declarative configuration tht matches the schema of the data	github
MindsDB	Python	Apache 2.0		MindsDB is a predictive platform that makes databases intelligent and machine learning easy to use	github
MLBox	Python	BSD-3-Clause	HPO	Accurate hyper-parameter optimization in high-dimensional space with support for distributed computing.	github
mljar	Python	MIT	HPO	Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation	github
Microsoft NNI	Python	MIT	FE/NAS/HPO/MS	NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture Search, Hyperparameter Tuning and Model Compression.	github
NASLib	Python	Apache 2.0	NAS	NASLib is a modular and flexible framework created with the aim of providing a common codebase to the community to facilitate research on Neural Architecture Search	github
Oboe	Python	BSD-3-Clause	HPO	An AutoML pipeline selection system to quickly select a promising pipeline for a new dataset	github
Optunity	Python with API for R and MATLAB	MIT	HPO	Optunity is a library containing various optimizers for hyperparameter tuning	github
Optuna	Python	MIT	HPO	An open source hyperparameter optimization framework to automate hyperparameter search	github
Orange	Python	GPL-v3	HPO	A data mining and visualization toolbox for novice and expert alike. To explore data with Orange, one requires no programming or in-depth mathematical knowledge	github
PyCaret	Python	MIT	HPO/MS/MT/ME	PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows	github
Prophet	Python	MIT	Time Series Forecast	Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects	github
RoBO	Python	BSD-3-Clause		RoBO is a flexible framework for robust Bayesian Optimization	github
Scikit-Optimize	Python	Open Source	HPO	A simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization.	github
SMAC3	Python	BSD-3-Clause	HPO	Sequential Model Algorithm Configuration (SMAC) is a tool for algorithm configuration to optimize the parameters of arbitrary algorithms, including hyperparameter optimization of Machine Learning algorithms	github
TPOT	Python	GPL-v3	HPO/MS/NAS	TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming	github
Xcessiv	Python	Apache 2.0		A web-based application for quick, scalable, and automated hyper-parameter tuning and stacked ensembling in Python	github
ZazuML	Python	MIT	MS/HPO/AS	This is an easy to use open-source AutoML framework for object detection	github
ZenML	Python	Apache 2.0	MLOps Workflow	ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines	github
Auto-WEKA	Java	GPL-v3		Auto-WEKA is Automated model selection and hyper-parameter tuning for Weka models	github
H2O AutoML	Java with Python, Scala & R APIs and web GUI	Apache 2.0		Automated: data prep, hyperparameter tuning, random grid search and stacked ensembles in a distributed ML platform	github
Advisor	Julia	MIT		AutoMLPipeline (AMLP) is a package that makes it trivial to create complex ML pipeline structures using simple expressions	github
TransmogrifAI	Scala	GPL-v3		AutoML library written in Scala that runs on top of Apache Spark. It was developed with a focus on accelerating machine learning developer productivity through machine learning automation	github
BayesOpt	C++	GPL-v3		BayesOpt is an efficient implementation of the Bayesian optimization methodology for nonlinear optimization, experimental design and hyperparameter tunning	github
Tangram		MIT		Tangram is the all-in-one machine learning toolkit for programmers	github

Companies Tools

Name	Language	License*	AutoML Tasks**	Short Description	Source
Aible	-	Commercial		"Aible is the Only ROI Optimized Auto Machine Learning platform that Delivers Real Business Impact Through Seamless Collaboration."	link
Akkio	-	Commercial	NLP Tasks	"Grow your business with data-driven decisions. Go from data to AI in 10 minutes, no code or data science skills required."	link
Amazon Lex	Python	Commercial	NLP Tasks	"Automatically build machine learning models with speed and scale"	link
Auger.AI	-	Commercial		"Auger.AI is the most accurate AutoML platform. Auger’s patented Bayesian optimization-based search of algorithm/hyperparameter combinations builds more accurate predictive models faster."	link
Azure AutoML	Python	Commercial	NLP Tasks	"Easily add AI that understands intent, maintains context, and automates simple tasks across many languages"	link
bigml	-	Commercial	HPO	"Rapidly bring your predictive modeling tasks to production through effective automation. BigML turns the difficult, time-consuming work of hand-tuning models or executing complex workflows into one-click menu options or single API calls"	link
clarifai	-	Commercial	HPO	"Clarifai enables developers, data scientists, and machine learning engineers as well as non-technical users, to automatically build scalable machine learning models."	link
compellon	-	Commercial		"Compellon was built for business experts so they can solve business problems without the need for technical knowledge."	link
DataRobot	Python	Commercial		"DataRobot’s Automated Machine Learning (AutoML) solution empowers AI Creators at many organizations to apply their domain expertise and deliver best-in-class models without sacrificing time and trust"	link
dataiku	Python	Commercial		"Automating the model training process using the best practice techniques combined with built-in guardrails allows business analysts to build and compare multiple production-ready models. Dataiku AutoML uses leading algorithms and frameworks like Scikit-Learn and XGBoost to find the best modeling results in an easy to use interface for users across the business."	link
DMway	-	Commercial		"Meet the practical solution that allows anyone in the organization to create valuable predictive analytics and machine learning algorithms, without the complexity of data science."	link
dotData	-	Commercial		"dotData Enterprise is an enterprise-grade AI automation platform. It streamlines Automated Feature Engineering and Automated Machine Learning technologies and offers no-code end-to-end automation. BI & Analytics teams can leverage dotData Enterprise to build predictive models in days to make dashboards predictive and actionable."	link
B2Metric	-	Commercial	HPO/MS	"B2Metric automates data cleaning, feature engineering and model selection process with supervised, unsupervised, semi-supervised algorithms."	link
enhencer	-	Commercial		"Predict and monitor the most valuable visitors according to your business with a self-learning dynamic AI-Engine."	link
Google Vertex AI (AutoML)	Python	Commercial		"Train high-quality custom machine learning models with minimal effort and machine learning expertise"	link
JADBio	Python	Commercial		"JADBio is a state-of-the-art automated Machine Learning Platform, designed for Life Scientists, enabling them to effortlessly make new discoveries and extract knowledge from publicly available or own-study data, without the need for coding"	link
H2O.ai	Java with Python, Scala & R APIs and web GUI	Commercial		"H2O AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit."	link
IBM	-	Commercial		"Build and scale trusted AI on any cloud. Automate the AI lifecycle for ModelOps."	link
Kortical	-	Commercial		"Kortical’s competition crushing, cloud scale, distributed AutoML will start finding the best machine learning solution for your dataset."	link
MateLabs	Python	Commercial		"Mateverse is our home grown AutoML platform to create scalable & highly optimized ML Solutions rapidly . Our AutoML tech is 100x faster and extremely low touch needing no manual intervention"	link
MLJAR Studio	Python	Commercial		"MLJAR Studio is a desktop application. It allows you to create Python notebooks with no-code graphical interface."	link
Nyckel	Python	Commercial	NLP Tasks	"The lightning fast machine learning platform for developers"	link
oneclick.ai	Python	Commercial		"Our neural network architecture search algorithm will find the optimal model for extracting insights from your data"	link
Prevision.io	-	Commercial		"Prevision.io brings powerful AI management capabilities to data science users so more AI projects make it into production and stay in production."	link
RapidMiner		Commercial		"Create predictive models in 5 clicks using automated machine learning and data science best practices"	link
TAZI.ai		Commercial		"TAZI’s AutoML platform is designed with the user in mind. Major ML techniques that need several lines of coding are now one click away instead. We call this our One-Click Philosophy, all functionalities are designed to be one click away for the user."	link
TIMi Modeler	Python, R, JavaScript	Commercial		"Using Modeler is the guarantee to see your models leave the “data lab” and go into production, in one drag & drop."	link
Splunk		Commercial		"Create predictive models in 5 clicks using automated machine learning and data science best practices"	link
Xpanse AI		Commercial		"Automated Data Science platform delivering Predictive Models from complex databases in minutes."	link

Use Cases

We have used TPOT as a final meta-learner step to blend the classification/regression outputs of many MLPs: https://arxiv.org/pdf/1808.07069.pdf
We compared the performance of TPOT, Auto-Keras, Auto-PyTorch and auto-sklearn vs step-by-step creation of some common algorithms: https://arxiv.org/pdf/2001.08118.pdf
We have used TPOT as the ML main tool in a supervised project to unveil some galaxies properties: https://arxiv.org/pdf/2111.01185.pdf
We have also combined some AutoML tools such as Pycaret/TPOT and Auto-Keras/TPOT to achieve improved performance.