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Automated machine learning

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Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML.[1]

AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning.[2][3] The high degree of automation in AutoML aims to allow non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models.[4]

Common techniques used in AutoML include hyperparameter optimization, meta-learning and neural architecture search.

  1. ^ Spears, Taylor; Bondo Hansen, Kristian (2023-12-18), "The Use and Promises of Machine Learning in Financial Markets", The Oxford Handbook of the Sociology of Machine Learning, Oxford University Press, doi:10.1093/oxfordhb/9780197653609.013.6, ISBN 978-0-19-765360-9, retrieved 2024-06-10
  2. ^ Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013). Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms. KDD '13 Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 847–855.
  3. ^ Cite error: The named reference AutoML2014ICML was invoked but never defined (see the help page).
  4. ^ Olson, R.S., Urbanowicz, R.J., Andrews, P.C., Lavender, N.A., Kidd, L.C., Moore, J.H. (2016). Automating Biomedical Data Science Through Tree-Based Pipeline Optimization. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. doi:10.1007/978-3-319-31204-0_9
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