Auto-WEKA¹

Hyperopt-sklearn²

Auto-sklearn³

Auto-PyTorch⁴

TPOT⁵

Feature Tools⁶

tsfresh⁷

FeatureHub⁸

ATM⁹

Auto_ml¹⁰

H2O AutoML¹¹

SAS¹²

MLBox¹³

Google AutoML¹⁴

Azure Machine Learning¹⁵

Amazon SageMaker¹⁶

AutoKeras¹⁷

TransmogrifAI¹⁸

Darwin¹⁹

MLJar²⁰

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Footnotes

1. Kotthoff L, Thornton C, Hoos HH, Hutter F, Leyton-Brown K (2019) Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA. in Hutter F, Kotthoff L, Vanschoren J, (Eds.). Automated Machine Learning. Springer International Publishing. Cham, pp. 81–95. ↩

2. Komer B, Bergstra J, Eliasmith C (2019) Hyperopt-Sklearn. in Hutter F, Kotthoff L, Vanschoren J, (Eds.). Automated Machine Learning. Springer International Publishing. Cham, pp. 97–111. ↩

3. Feurer M, Klein A, Eggensperger K, Springenberg JT, Blum M, Hutter F (2019) Auto-sklearn: Efficient and Robust Automated Machine Learning. in Hutter F, Kotthoff L, Vanschoren J, (Eds.). Automated Machine Learning. Springer International Publishing. Cham, pp. 113–134. ↩

4. Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, Matthias Urban, Michael Burkart, Max Dippel, Marius Lindauer, Frank Hutter (2018) Towards Automatically-Tuned Deep Neural Networks: 7. in Hutter F, Kotthoff L, Vanschoren J, (Eds.). AutoML: Methods, Sytems, Challenges. Springer, pp. 141–156. ↩

5. Olson RS, Moore JH (2019) TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning. in Hutter F, Kotthoff L, Vanschoren J, (Eds.). Automated Machine Learning. Springer International Publishing. Cham, pp. 151–160. ↩

6. Feature Labs. Featuretools 0.12.0 documentation. https://docs.featuretools.com/en/stable/index.html (accessed on 20.11.2019). ↩

7. Christ M, Braun N, Neuffer J. tsfresh — tsfresh 0.12.0 documentation. https://tsfresh.readthedocs.io/en/latest/index.html (accessed on 06.02.2020). ↩

8. Smith MJ, Wedge R, Veeramachaneni K (2017) FeatureHub: Towards collaborative data science. 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 590–600. ↩

9. Swearingen T, Drevo W, Cyphers B, Cuesta-Infante A, Ross A, Veeramachaneni K (2017 - 2017) ATM: A distributed, collaborative, scalable system for automated machine learning. 2017 IEEE International Conference on Big Data (Big Data). IEEE, pp. 151–162. ↩

10. Parry P. auto_ml 0.1.0 documentation. https://auto-ml.readthedocs.io/en/latest/index.html (accessed on 20.11.2019). ↩

11. H2O.ai. AutoML: Automatic Machine Learning — H2O 3.26.0.10 documentation. http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html (accessed on 20.11.2019). ↩

12. SAS Institute Inc. SAS Visual Data Mining and Machine Learning. https://www.sas.com/en_us/software/visual-data-mining-machine-learning.html (accessed on 26.01.2020). ↩

13. ARONIO DE ROMBLAY A. MLBox Documentation. https://mlbox.readthedocs.io/en/latest/index.html (accessed on 20.11.2019). ↩

14. Google Cloud. Best practices for creating training data | AutoML Tables Documentation | Google Cloud. https://cloud.google.com/automl-tables/docs/data-best-practices#tables-does (accessed on 20.11.2019). ↩

15. Microsoft Azure. Azure Machine Learning documentation. https://docs.microsoft.com/en-us/azure/machine-learning/ (accessed on 20.11.2019). ↩

16. AWS. Amazon SageMaker - Developer Guide. https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf (accessed on 20.11.2019). ↩

17. Feurer M, Klein A, Eggensperger K, Springenberg JT, Blum M, Hutter F (2019) Auto-sklearn: Efficient and Robust Automated Machine Learning. in Hutter F, Kotthoff L, Vanschoren J, (Eds.). Automated Machine Learning. Springer International Publishing. Cham, pp. 113–134. ↩

18. Salesforce.com, Inc. AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark from Salesforce Engineering. https://transmogrif.ai/ (accessed on 26.01.2020). ↩

19. SparkCognition (2019) From Data to Application: DARWINS UNIQUE APPROACH TO AUTOML. ↩

20. MLJAR. mljar-docs. https://docs.mljar.com/ (accessed on 06.02.2020). ↩