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What is AutoML (Automated Machine Learning)?

AutoML - Automatisiertes maschinelles Lernen

AutoML enjoys a steadily increasing popularity (see Forbes). Not least driven by the numerous successes in practical analyses. In a world in which more and more devices produce data and are networked with each other, the data “produced” grows disproportionately. Therefore AutoML is of urgent necessity to gain knowledge from these rapidly increasing data on time. We assume that AutoML becomes even more critical in the coming years and that the analysis methods deliver even more precise and faster results. The field of activity of the data scientist will not disappear, but rather, his focus will shift to more specific or sophisticated analysis techniques. In short: AutoML saves time and money (you don’t need a larger team of data science and machine learning experts). It is also the easiest and cheapest way to enter the world of artificial intelligence or machine learning.

Features of AutoML

Features of AutoML

AISOMA – Features of AutoML

So what is AutoML?

Automated Machine Learning (AutoML) is the process of automating the end-to-end process of applying Machine Learning to real-world problems. In a typical machine learning application, experts must apply the appropriate methods of data preprocessing, feature engineering, feature extraction, and feature selection to make the data set used for machine learning. Following these preprocessing steps, practitioners must then perform the algorithm selection and hyper-parameter optimization to maximize the predictive performance of the final machine learning model. Since many of these steps often go beyond the capabilities of laypersons, AutoML has been developed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. Automating the end-to-end process of applying machine learning offers the benefits of producing more straightforward solutions, faster creation of these solutions, and models that often outperform hand-designed models.

 

Comparison of Traditional Machine Learning Workflow and AutoML Workflow.

AutoML Workflow (source)

Objectives of automation:

Automated machine learning can capture different phases of the machine learning process:

Below is a list of AutoML vendors:

(Note: The list represents only a small selection of providers.)

AISOMA

See also: 8 Useful Industry 4.0 Slides

 

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