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Data Management // Big Data Analytics

Big Data Analytics For The Masses

BigML's cloud-hosted machine-learning platform for predictive analytics is designed for everyone -- from data gurus to regular business folks.

5 Big Wishes For Big Data Deployments
5 Big Wishes For Big Data Deployments
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Machine learning has historically resided within the realm of the data scientist, a Ph.D.-wielding expert trained to glean insights from big data. But with the rapid expansion of digital information, a move is on to democratize data science tools and put the average business analyst on par (almost) with the data expert.

That's the mission of BigML, a Corvallis, Ore.-based startup with a SaaS-based machine learning platform that allows everyday business users to create actionable predictive models within minutes.

BigML opened for business in October 2012 and currently has 3,500 registered users, approximately half of whom have created models. More than 20% of those users have uploaded their own data sources, the company says.

[ Business's big data needs are increasing faster than ever. Is your organization keeping up? See CIO Tough Love: Agility Demands Increasing. ]

BigML was created by experts in machine learning and recommendation systems, says Andrew Shikiar, the company's VP of corporate development. Francisco Martin, BigML's founder, previously started Strands, a technology recommendation provider.

Of course, BigML isn't the only company offering a cloud-based advanced analytic platform. Competitors include Precog, Alteryx, and SkyTree.

BigML's underlying algorithms produce interactive decision trees. "We're a big fan of decision trees. We think it's the most powerful, intuitive way for someone to do predictive analyses," said Shikiar in a phone interview with InformationWeek.

The service is free to try. To get started, you upload data (e.g., an Excel spreadsheet) to BigML's website and create a data set. (Setup can get tricky for novices, and video tutorials are provided.) BigML's interface has several easy-to-use tools such as "one-click" buttons for creating data sets and predictive models. You can change things about your data as well, such as the data set's descriptive name and parsing options.

After creating a model, you can download it for local usage, or share and/or sell it in BigML's public gallery. "There's a big social component to what we're doing, which is the ability to share your model. I can make [a model] available to anybody to use or buy through the gallery on our website," said Shikiar.

Potential BigML customers include healthcare, financial service, and insurance providers, as well as online marketers. "You can analyze clickstream data on your website," Shikiar said. "You can analyze how well your email list is performing. Or better yet, based on your sales results, you can determine which segments of your email list to target."

Developers can build BigML's functionality into enterprise/consumer services and applications as well. "We think it's powerful in a couple of ways. One, it's good for teams in enterprises," said Shikiar. "And if you have a model that's very interesting, you can post it and let other people access it either for free, or for a fee."

BigML's machine-learning tools are limited at this early stage, but the company says there's a lot more to come. "In the future, we'll support different algorithms, not just decision trees. We may support things like clustering, neural networks, things like that," Shikiar said. "We're a small company, so we're focusing on what we think is most important and useful for the marketplace."

BigML's fees are based on the number of "credits" that a customer uses. For instance, 1 TB of space to create datasets equals 1,048,576 credits; 1 TB of data to create new models is 4,194,304 credits; and you'll need 10,000 credits to generate 1 million predictions. The credit total: 5,252,880. Multiple that number by 0.1 cents, and your bill comes to $5,252.88.

"We're constantly layering in new features and functions," added Shikiar. "You'll also see the business-oriented aspects of Big ML, things that are targeted squarely at SMB and enterprise users for team sharing and utilization."

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