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How machine learning ate Microsoft

Mary Branscombe | Feb. 23, 2015
Yesterday's announcement of Azure Machine Learning offers the latest sign of Microsoft's deep machine learning expertise -- now available to developers everywhere.

That experience offers a unique advantage, says Barga: "These algorithms have been hardened and proven over the years. We're able to draw on that expertise to implement it again in Azure ML. We know what the best practices are, what the heuristics are, what should we do to ensure this will be robust, scalable, and performant implementation we can deliver to our customers."

But Azure ML didn't merely take the machine learning algorithms MSR had already handed over to product teams and stick them into a drag-and-drop visual designer. Microsoft has made the functionality available to developers who know the R statistical programming language and Python, which together are widely used in academic machine learning. Microsoft plans to integrate Azure ML closely with Revolution Analytics, the R startup it recently acquired.

Developers can also design a machine learning system using the Azure ML Studio tool. That's popular even with experienced machine learning developers like the team at Mendeley, Elsevier's academic research network, which built a new recommendation system in a third of the time it took them with other tools. JJ Food Service in the United Kingdom used it to make a predictive shopping cart that puts products in for you; customers like the convenience and revenue is up 5 percent.

A machine that trains itself
In order to allow easier use of multiple machine learning algorithms together, Microsoft needed to build a suitable platform. That meant creating a system for moving new algorithms from research into production; as new techniques are developed, they can be plugged into Azure ML, keeping it up to date as machine learning continues to develop.

A common problem with older machine learning systems (and one of the issues that deep learning will address) is "ML rot." In other words, you spend a long time training your system, and when you roll it out, it works for a while -- but it falls out of date and you have to train it again. One way to avoid that is by retraining your model as you use it.

During the preview, customers were so keen on that idea that Microsoft added programmatic training and retraining. "They want to upload data to an API and have machine learning models do the learning, so we added that," explains Sirosh. "Once you have an API in place, you can keep uploading data and the model will update itself and stay fresh and be constantly learning."

That's what eBay used to train its translation system on the terms used in women's fashion. If you're selling handbags, dresses, shoes, or other fashion items on eBay, you might see much better sales overseas because automatic translations of listings are more accurate -- and available in all 45 languages Azure ML supports.


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