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.
To train this model, data from the organization's HR will have to be used. But for the data to be useful, they have to be sufficient in quantity and quality, which is not always the case. This is where Explorium comes in.
Initially, users connect a dataset with a target column, indicating what they would like to predict. Multiple internal sources can be connected, as long as one of them contains the target column. Then Explorium detects the meaning of the columns for each input dataset and enriches the dataset with additional data sources.
For example, if the engine identifies a location coordinate in the columns of the customer's data (latitude and longitude), it would enrich his data with Geo-Spatial taggings (such as competitors in the area), demographic sources, and so on.
Before getting to how this identification works, it's worth pondering where does that data come from, and how their relevance and reliability is assessed. Explorium sources data from multiple channels. Some of it is open and public datasets, but there's more.
How does Explorium work?
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