PredictionIO today announces the finalization of $2.5M in funding in a capital raise whose investors include Azure Capital QuestVP, CrunchFund, Stanford StartX-Fund, Kima Ventures, IronFire, Sood Venture and XG Ventures. The funding will be used to accelerate product development and marketing and sales and operations for the company’s open source machine learning server for predictive analytics. PredictionIO aspires to fill the role in the predictive analytics space played by MySQL in the relational database space by delivering an open source platform that empowers data scientists to both leverage a pre-defined library of predictive algorithms as well as create new algorithms that they can either choose to contribute to the platform, or keep to themselves. Built using Scala, the PredictionIO platform supports JVM and Java-based code as well as backend Hadoop-based data. Typical use cases for PredictionIO’s technology include the production of personalized content and recommendation engines, as well as algorithms that predict the behavior of users and industries based on historical trends. Available through the Amazon Web Services marketplace or via download, Prediction IO already boasts an open source user community of over 4000 developers and undergirds predictive analytics in “hundreds” of applications across of variety of verticals. The platform fills a critical niche in the big data analytics space by delivering an open source platform as a service-like infrastructure for the development of predictive analytics. Importantly, PredictionIO empowers companies who cannot afford to hire quant-level data scientists to quickly develop and tweak predictive models using its guided, machine learning-based user interface. That said, much of the success of PredictionIO will depend on the richness and variety of its library of pre-configured predictive modeling algorithms, but its initial round of funding represents a promising start toward accelerating adoption and expanding the platform’s impressive list of existing libraries and relevance for various use cases.
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