Automated machine learning (AutoML), as the name suggests, is the automation of the process for constructing machine learning models. Its use is best illustrated by comparing it to the traditional means for constructing ML models. Without AutoML, the following tasks are left to the modeler to determine based on their understanding of the problem, desired model performance, and most critically their expertise in the proper application of signal processing and machine learning classifiers:
- Deciding how to segment for regions of interest in the input data
- Determining which pre-processing and feature transforms are needed to convert raw input data into a suitable input vector for the classifier
- Selecting which type of machine learning classifier to use to deliver best results
- Optimizing model parameters and tuning of hyperparameters
- Assessing the need for post-processing to further enhance model performance
In contrast, AutoML employs high performance computing and use of search optimization algorithms to augment human know-how in performing the task of constructing an ideal ML model for a given application and train/test dataset. The advantages of AutoML include the ability to evaluate hundreds of thousands to millions of model permutations in the same timeframe as a human data science expert may be able to evaluate only a handful. And with directed search constraints, the combination of AutoML in the hands of a skilled user can focus searches on the most promising permutations rather than just execute brute-force grid searches. The upshot is that AutoML can be a powerful ally in algorithm development whether empowering an AI novice or extending the capabilities of a seasoned data science expert.