Automated machine learning

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the growing challenge of applying machine learning.[1][2] The high degree of automation in AutoML aims to allows 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.

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Machine learning
and data mining
Problems
Supervised learning
(classification • regression)
Clustering
Dimensionality reduction
Structured prediction
Anomaly detection
Artificial neural network
Reinforcement learning
Theory
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Comparison to the standard approach

In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form that all algorithms can be applied to it. To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods. After these steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their model. Each of these steps may be challenging, resulting in significant hurdles to using machine learning.

AutoML wants to simplify these steps for non-experts.

Targets of automation

Automated machine learning can target various stages of the machine learning process.[2] Steps to automate are:

See also

References
  1. 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.
  2. Hutter F, Caruana R, Bardenet R, Bilenko M, Guyon I, Kegl B, and Larochelle H. "AutoML 2014 @ ICML". AutoML 2014 Workshop @ ICML. Retrieved 2018-03-28.

Further reading
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