Open source tools replace data scientists
Machine learning requires programming and expensive data analysts for processing. Researchers at MIT are currently developing a simpler solution01 Apr. 2018 Tim Stockschläger
The Massachusetts Institute of Technology is promoting its new ML 2.0 concept with the slogan "Machine learning for many". The scientists are cooperating with the company’s own spin-off Feature Labs on a process to make the usually tedious, complex and costly implementation of ML implementations profitable sooner, as well as financially feasible for companies smaller than groups.
They divide the ML processes of the preparation into concrete individual steps and automate the individual stages. The approach has already resulted in feature tools , which industrial companies use to make big data stocks ML-ready. The tools are open source and available on GitHub . The major advantage is that whereas previously several data scientists were needed, engineers can now develop applications on their own: "We want to make machine learning accessible to as many people as possible," says Project Coordinator Kalyan Veeramachaneni about their goal.
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