AutoML System

Survey

A curated list of automated machine learning papers, articles, tutorials, slides and projects [GitHub]
Taking human out of learning applications: A survey on automated machine learning. [Must Read Survey]
- Quanming, Y., Mengshuo, W., Hugo, J.E., Isabelle, G., Yi-Qi, H., Yu-Feng, L., Wei-Wei, T., Qiang, Y. and Yang, Y.
AutoML Freiburg-Hannover [Website]
Survey on End-To-End Machine Learning Automation [Paper] [GitHub]
Design Automation for Efficient Deep Learning Computing [paper] [GitHub]
- Han, Song, et al. (arXiv preprint arXiv:1904.10616 (2019))

AutoML Opensource Toolkit

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]

Auto Model Selection

Automating model search for large scale machine learning [Paper]
- Sparks, E.R., Talwalkar, A., Haas, D., Franklin, M.J., Jordan, M.I. and Kraska, T., 2015, August.
- In Proceedings of the Sixth ACM Symposium on Cloud Computing (pp. 368-380). ACM.
A framework for searching a predictive model [Paper]
- Takahashi, Yoshiki, Masato Asahara, and Kazuyuki Shudo
- In SysML Conference, vol. 2018. 2018.
Dynamic Autoselection and Autotuning of Machine Learning Models for Cloud Network Analytics [Paper]
- IEEE Transactions on Parallel and Distributed Systems 30, no. 5 (2018): 1052-1064.
- Karn, Rupesh Raj, Prabhakar Kudva, and Ibrahim Abe M. Elfadel.