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Accelerating data science with Apache SystemML
Using machine learning algorithms, applications can grow and change when exposed to new data, opening a gateway into the era of cognitive computing. Even now, many organizations are using machine learning to overcome business challenges:
- Marketing personnel can use machine learning to personalize their company’s approach to individuals in the market while dynamically shifting content to take advantage of moment-by-moment opportunities.
- Organizations that are plugged into the Internet of Things can use data points provided by their devices to automate things as varied as braking in vehicles and levels of patient medication.
- Financial organizations can use predictive analytics to reimagine their financial planning process. Whereas traditional finance software generally takes a historical view when attempting to predict the future, predictive analytics systems can provide dynamic views into current opportunities.
Indeed, machine learning is becoming many companies’ preferred way of churning through the massive amounts of data made accessible by the Internet of Things. To find out what machine learning could do for your organization, check out SystemML, an Apache incubation project announced in November 2015 as the outgrowth of IBM research, then join us for an in-depth look provided by Fred Reiss, a lead researcher in the IBM Silicon Valley Research Lab, one of the foremost minds in machine learning.
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