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Explorium, a Tel Aviv-based startup developing what it describes as an automated data and feature discovery platform, today announced that it’s raised $19 million total across several funding rounds. Emerge and F2 Capital contributed $3.6 million in a seed raise, and Zeev Ventures led a $15.5 million in series A.

The influx of capital comes after a banner year for Explorium, during which says it nabbed Fortune 100 customers in industries ranging from financial services to consumer packaged goods, retail, and ecommerce. “We are doing for machine learning data what search engines did for the web,” said Explorium CEO Maor Shlomo, who together with cofounders Or Tamir and Omer Har previously led large-scale data mining and organization platforms for IronSource, Natural Intelligence, and the Israel Defense Forces’ 8200 intelligence unit.

Explorium’s platform acts like a repository for all of an organization’s information, connecting siloed internal data to thousands of external sources on the fly. Using machine learning, it automatically extracts, engineers, aggregates, and integrates the most relevant features from data to power sophisticated predictive algorithms, evaluating hundreds before scoring, ranking, and deploying the top performers.

Lenders and insurers can use Explorium to discover predictive variables from thousands of data sources, Shlomo explains, while retailers can tap it to forecast which customers are likely to buy each product. “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,” he added.


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Within Explorium, data scientists can add custom code to incorporate domain knowledge and fine-tune AI models. Additionally, they’re afforded access to tools designed to help uncover optimization-informing patterns from large corpora.

“Explorium’s vision of empowering data scientists by finding relevant data from every possible source in scale and thus making models more robust is creating a paradigm shift in data science,” said Emerge founding partner Dovi Ollech. “Working with the team from the very early days made it clear that they have the deep expertise and ability required to deliver such a revolutionary data science platform.”

Explorium joins a raft of other startups and incumbents in the burgeoning “auto ML” segment. Databricks just last month launched a toolkit for model building and deployment, which can automate things like hyperparameter tuning, batch prediction, and model search. IBM’s Watson Studio AutoAI — which debuted in June — promises to automate enterprise AI model development, as does Microsoft’s recently enhanced Azure Machine Learning cloud service and Google’s AutoML suite.

IDC predicts that worldwide spending on cognitive and AI systems will reach $77.6 billion in 2022, up from $24 billion in revenue last year. Gartner agrees: In a recent survey of thousands of businesses executives worldwide, it found that AI implementation grew a whopping 270% in the past four years and 37% in the past year alone.

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