According to Michigan State University and MIT, automated machine learning system analyses the data and deliver a solution 100x faster than one human. The automated machine learning platform which is known as ATM (Auto Tune Models) uses cloud-based, on demand computing to accelerate data analysis.
Researchers of MIT tested the system through open-ml.org, a collaborative crowdsourcing platform, on which data scientists collaborate to resolve problems. They found that ATM evaluated 47 datasets from the platform and the system was capable to deliver a solution that is better than humans. It took nearly 100 days for data scientists to deliver a solution, while it took less than a day for ATM to design a better-performing model.
"There are so many options," said Ross, Franco Modigliani professor of financial economics at MIT, told MIT news. "If a data scientist chose support vector machines as a modeling technique, the question of whether she should have chosen a neural network to get better accuracy instead is always lingering in her mind."
ATM searches via different techniques and tests thousands of models as well, analyses each, and offers more resources that solves the problem effectively. Then, the system exhibits its results to help researchers compare different methods. So, the system is not automating the human data scientists out of the process, Ross explained.
"We hope that our system will free up experts to spend more time on data understanding, problem formulation and feature engineering," Kalyan Veeramachaneni, principal research scientist at MIT's Laboratory for Information and Decision Systems and co-author of the paper, told MIT News.
Auto Tune Model is now made available for companies as an open source platform. It can operate on single machine, on-demand clusters, or local computing clusters in the cloud and can work with multiple users and multiple datasets simultaneously, MIT noted. "A small- to medium-sized data science team can set up and start producing models with just a few steps," Veeramachaneni told MIT News.
Source: MIT Official Website