On Automated Machine Learning

Emphasizing the automated, inevitable that such method will be more broadly integrated with general IT analytics.  But will also require automated updating of their use in context.

AutoML: Not A Magic Bullet, But A Powerful Business Tool  by 7wData

When AI was first introduced into business processes, it was transformative, enabling companies to leverage the vast amounts of accumulated data to improve planning and decision making. It soon became apparent, however, that integrating AI into business processes at scale required significant resources. First, companies had to recruit highly sought-after (and highly paid) data scientists to create the data models behind AI. Second, the process of building and training the machine learning models that accelerated the data analysis process required a significant expenditure of time and energy. This, in turn, led to the development of automated machine learning (AutoML), techniques that essentially automate core aspects of the machine learning process including model selection, training, and evaluation.

In effect, AutoML seeks to trade machine (processing) time for human time. This automation brings many benefits. First and foremost, it decreases labor costs. It also reduces human error, automates repetitive tasks, and enables the development of more effective models. By reducing the technical expertise required to create an ML model, AutoML also lowers the barriers to entry, enabling business analysts to leverage advanced modeling techniques — without assistance from data scientists. And by relieving data scientists from repetitive tasks of the machine learning process, AutoML frees these costly resources to pursue higher-value projects.  ... "