Advances in Automated Machine Learning

Automated machine learning is inevitable.  How good will it be, and how much human oversight needs to be applied to ensure confidence in their results is important.  This article is a  good overview of work underway.  Its no only about searching for the right model,  in context of needed goals, its also about maintaining the interaction between data and solutions.   Not too unlike the use of any kind of analytics optimization, which has been studied for years.

Cracking open the black box of automated machine learning
Interactive tool lets users see and control how automated model searches work.
 By Rob Matheson | MIT News Office 

Researchers from MIT and elsewhere have developed an interactive tool that, for the first time, lets users see and control how automated machine-learning systems work. The aim is to build confidence in these systems and find ways to improve them.

Designing a machine-learning model for a certain task — such as image classification, disease diagnoses, and stock market prediction — is an arduous, time-consuming process. Experts first choose from among many different algorithms to build the model around. Then, they manually tweak “hyperparameters” — which determine the model’s overall structure — before the model starts training.

Recently developed automated machine-learning (AutoML) systems iteratively test and modify algorithms and those hyperparameters, and select the best-suited models. But the systems operate as “black boxes,” meaning their selection techniques are hidden from users. Therefore, users may not trust the results and can find it difficult to tailor the systems to their search needs.

In a paper presented at the ACM CHI Conference on Human Factors in Computing Systems, researchers from MIT, the Hong Kong University of Science and Technology (HKUST), and Zhejiang University describe a tool that puts the analyses and control of AutoML methods into users’ hands. Called ATMSeer, the tool takes as input an AutoML system, a dataset, and some information about a user’s task. Then, it visualizes the search process in a user-friendly interface, which presents in-depth information on the models’ performance.

“We let users pick and see how the AutoML systems works,” says co-author Kalyan Veeramachaneni, a principal research scientist in the MIT Laboratory for Information and Decision Systems (LIDS), who leads the Data to AI group. “You might simply choose the top-performing model, or you might have other considerations or use domain expertise to guide the system to search for some models over others.”

In case studies with science graduate students, who were AutoML novices, the researchers found about 85 percent of participants who used ATMSeer were confident in the models selected by the system. Nearly all participants said using the tool made them comfortable enough to use AutoML systems in the future.  ... " 

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