PredictionIO Raises $2.5M For Its Open Source Machine Learning Server

Aiming to do for Machine Learning what MySQL did for database servers, U.S. and UK-based PredictionIO has raised $2.5 million in seed funding from a raft of investors including Azure Capital, QuestVP, CrunchFund (of which TechCrunch founder Mike Arrington is a Partner), Stanford University‘s StartX Fund, France-based Kima Ventures, IronFire, Sood Ventures and XG Ventures. The additional capital will be used to further develop its open source Machine Learning server, which significantly lowers the barriers for developers to build more intelligent products, such as recommendation or prediction engines, without having to reinvent the wheel.

Being an open source company — after pivoting from offering a “user behavior prediction-as-a-service” under its old TappingStone product name — PredictionIO plans to generate revenue in the same way MySQL and other open source products do. “We will offer an Enterprise support license and, probably, an enterprise edition with more advanced features,” co-founder and CEO Simon Chan tells me.

The problem PredictionIO is setting out to solve is that building Machine Learning into products is expensive and time-consuming — and in some instances is only really within the reach of major and heavily-funded tech companies, such as Google or Amazon, who can afford a large team of PhDs/data scientists. By utilising the startup’s open source Machine Learning server, startups or larger enterprises no longer need to start from scratch, while also retaining control over the source code and the way in which PredictionIO integrates with their existing wares.

In fact, the degree of flexibility and reassurance an open source product offers is the very reason why PredictionIO pivoted away from a SaaS model and chose to open up its codebase. It did so within a couple of months of launching its original TappingStone product. Fail fast, as they say.

“We changed from TappingStone (Machine Learning as a Service) to PredictionIO (open source server) in the first 2 months once we built the first prototype,” says Chan. “As developers ourselves, we realise that Machine Learning is useful only if it’s customizable to each unique application. Therefore, we decided to open source the whole product.”

The pivot appears to be working, too, and not just validated by today’s funding. To date, Chan says its open source Machine Learning server is powering thousands of applications with 4000+ developers engaged with the project. “Unlike other data science tools that focus on solving data researchers’ problems, PredictionIO is built for every programmer,” he adds.

Other competitors Chan cites include “closed ‘black box” MLaaS services or software’, such as Google Prediction API,, BigML, and Skytree.

Examples of who is currently using PredictionIO include Le Tote, a clothing subscription/rental service that is using PredictionIO to predict customers’ fashion preferences, and PerkHub, which is using PredictionIO to personalize product recommendations in the weekly ‘group buying’ emails they send out.