Part machine learning platform, part data marketplace, Explorium promises to automate data and feature discovery, and build and deploy models for your analytics and application needs.

Machine learning is a powerful paradigm many organizations are utilizing to derive insights and add features to their applications, but using it requires skills, data, and effort. Explorium, a startup from Israel, has just announced $19 million of funding to lower the barrier on all of the above.

The funding announced today comprises a seed round of $3.6 million led by Emerge with the participation of F2 Capital and a $15.5 million Series A led by Zeev Ventures with the involvement of the seed investors. Explorium was founded by Maor Shlomo, Or Tamir, and Omer Har, three Israeli tech entrepreneurs, who previously led large-scale data mining and optimization platforms for big data-based marketing leaders.

“We are doing for machine learning data what search engines did for the web,” said Explorium co-founder and CEO Maor Shlomo. “Just as a search engine scours the web and pulls in the most relevant answers for your need, Explorium scours data sources inside and outside your organization to generate the features that drive accurate models.”

Explorium’s platform works in three stages: Data enrichment, feature engineering, and predictive modeling.

The first part of the process involves finding appropriate data for the task at hand. To train machine learning algorithms, relevant datasets are needed. Let’s say, for example, an organization is interested in devising a predictive model for HR, to help reduce churn by generating alerts and recommendations for action.

Read the full article on ZDNet