Imagining Tomorrow’s Workplace II: Machine Learning in the Cloud

By Koh Wanzi

“Historically, we’ve been in a world where computing was a scarce resource. Now it is moving to being an abundant resource,” says Greg DeMichillie, the director of product management for Google’s public cloud. Cloud computing allows companies to channel this abundance of computing power in the form of millions of computers in networks around the globe, enabling them to carry out complex tasks such as data processing and analysis with minimal manpower and costs.

Amazon, Google and Microsoft offer public cloud services to anyone able to rent space on the cloud, a boon to individuals and small companies. For instance, start-ups such as Pinterest and Snapchat were able to keep their costs down by running on cloud services provided by Amazon and Google respectively.

However, cloud computing is not the end game, and a rapidly developing form of artificial intelligence called machine learning is poised to help individuals and businesses handle their data stored in the cloud and efficiently automate processes that would ordinarily require considerable amounts of human input and effort.

Changing the Future

Machine learning utilises existing data to create models that can predict future behaviour or trends and is already in use in search engines, online product recommendations and Microsoft’s answer to Apple’s Siri – Cortana. However, with the launch of Microsoft’s Azure Machine Learning (ML) platform, the public will soon have access to the combined powers of cloud computing and machine learning.

“Traditional data analysis lets you predict the future. Machine learning lets you change the future,” says Joseph Sirosh, who heads the Azure ML project. According to Sirosh, cloud-based machined learning will enable people to do things from forecasting demand to predicting (and preventing) outbreaks of disease and crime. Many everyday processes will also be smartly automated, such as the scheduling of hospital services and anticipating when elevators need maintenance before they breakdown.

Freeing up Human Capital

Without the power of the cloud, machine learning traditionally required data scientists and considerable processing power, both hefty barriers to widespread adoption. Azure ML represents the first step towards making these analytic and processing capabilities available to a wider range of organisations. “Before [Azure ML], you needed data scientists to identify the data set, then have IT build an application to support that. This last part often took weeks or months to code and engineer at scale,” explains Sirosh. By making the computing power of the cloud available to machine learning processes, companies can build the same application within hours.

Azure ML is now being previewed by a select few partner companies. For example, Max 451 is using the platform to help an unnamed retail store predict customer demand so they can stock their stores in advance. Machine learning has also allowed Microsoft to engineer Skype Translate, a real-time online translation tool that can effectively teach itself to translate better, facilitating conferences and calls with foreign associates. Machine learning is quickly establishing itself as a key business tool, as it does away with the manual (and human) tedium at key points in operations and businesses.

Untapped Potential

We have barely begun to utilise the full power of cloud computing and machine learning, but their massive potential to revolutionise the way we work and do business is already abundantly clear. As we look forward into a future where such technology has become an entrenched norm, a smarter and ever more efficient economy and workforce beckons. The democratisation of powerful computing resources holds unlimited potential to change the way businesses and entire economies operate. In that future, the future may no longer have the ability to surprise.

What are your thoughts on machine learning? Share with us in the comment box below!

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