User:Mbule88/Automated machine learning
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[edit]Automated Machine Learning
[edit]Automated machine learning is often defined as the process of automating the time-consuming, iterative tasks of machine learning model development[1].
Challenges and Limitations
[edit]There are a number of key challenges being tackled around automated machine learning. A big issue surrounding the field is referred to as "development as a cottage industry"[2]. This phrase refers to the issue in machine learning where development relies on manual decisions and biases of experts. This is contrasted to the goal of machine learning which is to create systems that can learn and improve from their own usage and analysis of the data. Basically, it's the struggle between how much experts should get involved in the learning of the systems versus how much freedom they should be giving the machines. However, experts and developers must help create and guide these machines to prepare them for their own learning. To create this system, it requires labor intensive work with knowledge of machine learning algorithms and system design[3].
Additionally, some other challenges include meta-learning challenges[4] and computational resource allocation.
References
[edit]- ^ Microsoft (2023-06-07). "What is automated ML? AutoML - Azure Machine Learning". learn.microsoft.com. Retrieved 2023-12-03.
- ^ Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin, eds. (2019). Automated Machine Learning: Methods, Systems, Challenges. Springer Nature.
- ^ Glover, Ellen (2018). "Machine Learning with Python: Clustering". Built In. doi:10.4135/9781526466426.
- ^ "Meta Learning Challenges". metalearning.chalearn.org. Retrieved 2023-12-03.