Explorium Platform Billed as App Store for Predictive Models
As the need for more reliable model training grows, so also does investor interest in funding data science startups seeking to leverage automation to track down and match the appropriate data sets to a given model and application.
Among the platform developers gaining investors’ attention is Explorium, the Bay Area startup that this week announced a $31 million Series B funding round. So far, the three-year-old company has raised over $50 million. Lead investors include Zeev Ventures, 01 Advisors and Dynamic Loop.
One backer likens the platform to a storefront for predictive analytics.
Explorium’s value proposition is helping customers build better predictive models for machine learning by linking their internal data to external data sources ranging from behavioral and geospatial to time-series and web site data. The startup claims its data science platform can then automatically discover hidden connections and generate “custom signals” and features designed to make predictions more accurate.
“The greatest analytical challenge organizations will face over the next decade is finding the right data to feed their models and automated processes. The right data assets can make or break a company,” asserts Maor Shlomo, Explorium’s co-founder and CEO.
Among Explorium early investors is Adam Bain, former Twitter chief operating officer, and a partner at 01 Advisors. Bain described data science platform as akin to “an app store for predictive models—you throw them your model and its finds different data to make it work better.”
Founded in Tel Aviv, Israel, and now based in San Mateo, Calif., Explorium said it would use the latest investment to expand beyond early financial services applications to new business sectors and geographic markets.