What you can do with machine learning is almost limited only by your imagination. There are some obvious areas and the ML Studio service includes a lot of samples for building tools like recommendation engines that you can pull your own sales data into and connect to an ecommerce site.
One of the many teams inside Microsoft using ML Studio applied it to fraud detection around Windows licence keys. "They got an extra 20-30% savings on fraud," claims Sirosh.
He expects a lot of businesses to use it for sales forecasts, which he sees as a natural progression from storing your data in a database and analysing it with business intelligence tools.
"The great thing about machine learning is it allows you to learn from data and adapt to changing circumstances, and it's also predictive. Traditional analysis and analytics lets you look into the rear view mirror, lets you look at the past, analyse data by slicing and dicing it.
"Machine learning lets you interpret the future, looking forward at what is going to happen. You can forecast what is going to happen, you can forecast demand, you can forecast fraud. Once you have those early warnings and forecasts you can act on them."
Out of reach
That kind of prediction is normally out of reach for smaller businesses, he points out. "If you're trying to forecast demand for the product you're selling on your website for the next week, the traditional way smaller companies do this is in Excel; you work with your historical data and maybe you try to include seasonality in your spreadsheet." But Excel spreadsheets are fragile; formulae get out of data as things change, it's far too easy to accidentally mess up your data and it's difficult to manage a lot of data.
"With this tool you can do it at scale, with more historical data in the cloud, very simply. But then you put it into production with an API," he says. "It's an API that's in the cloud that can be called from applications and you get instant results that can feed into your inventory planning systems, into your ordering systems that get you more inventory. It's the automation that makes a huge difference. You want the automation to be able to serve this up in website and in apps and that now becomes possible, in a very simple way."
Sirosh hopes that will enable lots of new applications for machine learning. One interesting area is predictive maintenance for machinery, which he says is not widely used today. "In our labs we're working with data from escalator failure, and we found we could predict failure a week in advance of some major shutdowns."
If you know when a part was likely to fail, you could order a spare and have it ready to fit and save time, inconvenience and probably some significant repair bills. The same principle could save lives in healthcare.
Microsoft's research team in New England looked at hospital readmissions and discovered that a high percentage were people who hadn't understood how to take their drugs and a follow-up phone call was often enough to keep them out of hospital.
The potential is huge, Sirosh believes. "There are a large number of applications where forecasting and predictive anticipation is going to be an incredible empowering thing. Applications we haven't seen to today will rapidly emerge."
He's particularly excited about what we could do with devices and sensors. "Every mobile app can now be intelligent. Every Internet of Things sensor can now send data to the cloud and call into APIs that provide it with intelligence." But in every area you can think of, he's expecting an explosion of machine-learning powered improvements now so many more developers can experiment with it.
