Swearingen, Thomas, et al. "ATM: A distributed, collaborative, scalable system for automated machine learning." 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. [Paper][GitHub]
Google vizier: A service for black-box optimization. [Paper][GitHub]
Golovin, Daniel, et al. (SIGMOD 2017)
Aut-sklearn: Automated Machine Learning with scikit-learn [GitHub][Paper]
Katib: A Distributed General AutoML Platform on Kubernetes [GitHub][Paper]
NNI: An open source AutoML toolkit for neural architecture search and hyper-parameter tuning [GitHub]
AutoKeras: Accessible AutoML for deep learning. [GitHub]
Facebook/Ax: Adaptive experimentation is the machine-learning guided process of iteratively exploring a (possibly infinite) parameter space in order to identify optimal configurations in a resource-efficient manner. [GitHub]
DeepSwarm: DeepSwarm is an open-source library which uses Ant Colony Optimization to tackle the neural architecture search problem. [GitHub]
Google/AdaNet: AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models. [GitHub]
TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning [GitHub]
Angel-ML/automl:An automatic machine learning toolkit, including hyper-parameter tuning and feature engineering. [GitHub]