PredictionIO Raises $2.5M for Open Source Machine Learning Software

PredictionIO Raises $2.5M for Open Source Machine Learning Software

PredictionIO has received $25 million in seed funding for its open source machine learning platform, which could have big ramifications for data interpretation and decision-making.

PredictionIO, the open source machine learning platform, has received a big boost with the announcement of $2.5 million in seed funding, which it plans to use to make its automated data interpretation and prediction platform widely available to open source developers.

PredictionIO's goal is to make it easy for developers and companies of all sizes to integrate machine learning —i.e., software that can interpret data intelligently to make automated decisions and predictions—into their products. "PredictionIO aims to be the Machine Learning server behind every application," according to the company. "Building Machine Learning in software will be as common as search soon with PredictionIO."

That's a big claim, but PredictionIO now has $2.5 million in seed funding from Azure Capital, QuestVP, CrunchFund, Stanford StartX, Kima Ventures, IronFire, Sood Ventures and XG Ventures to back it. The funding was announced July 17.

According to PredictionIO, more than 4,000 programmers are currently involved in the development of its open source platform, which is already in use in "hundreds" of applications. Examples of companies now deploying the software to help make automated decisions about sales and marketing include Le Tote and PerkHub.

PredictionIO believes its ability to disrupt the channel lies in the open source nature of its product. Traditionally, it said, machine-learning software has been available only to companies that buy expensive, closed-source solutions, or that have the massive resources required to develop machine-learning code in-house. By allowing developers to integrate machine learning into any platform with minimal costs of time and money, PredictionIO stands to make the technology much more widespread and accessible.

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