Originally featured: Hewlett Packard Enterprise
What does it take to build a machine learning capacity?
- Less than you think
Machine learning as-a-service solutions can be an affordable alternative to expensive investments in technology and personnel.
Machine learning is the new game changer in business technology. In a world where digital information volumes are doubling every two years on average, machine learning allows organizations to extract highly valuable information from enormous data stores at heretofore unimaginable speeds.
Building and deploying machine learning solutions can be expensive, requiring investment in servers and storage, expanded networks, and data scientists. Alternatively, companies can invest in none of the above and turn to one of the many new machine learning as-a-service solutions. Getting started with machine learning in this way basically requires what virtually every organization is awash in today: data.
Healthcare's front line
Machine learning as a service isn't just for startups. With 21 hospitals, four teaching hospitals, and 29,000 employees, Edinburgh-based NHS Lothian is the second largest healthcare provider in the U.K. Its neonatal intensive care unit (NICU) captures enormous volumes of vital patient data that must be shared among doctors, nurses, dieticians, parents, and others. Speed is of the essence: In the NICU, the difference between life and death is often measured in minutes. Yet historically it took two hours or more for NHS Lothian to generate a single report manually.
NHS Lothian needed a machine language application that could scan patient data rapidly and generate automated reports. The hospital chain engaged Arria NLG a London-based company that delivers machine language applications as a service. The results have been striking. Report generation times have fallen from two hours to nearly real time, giving doctors and nurses the information they need to deliver the right care to their small patients.
Arria’s effectiveness comes down to how much data it receives, and how good that data is, says Ehud Reiter, chief scientist at Arria. Thousands of data sets are needed to “train” the system, as machine learning is an iterative process that improves over time with more data. But you don’t necessarily need more infrastructure or analytics experts to leverage it.
Machine learning: Lessons for leaders
- Machine learning as-a-service solutions can be an affordable alternative to expensive investments in technology and personnel.
- Getting started with a machine learning vendor requires little more than what virtually every organization is awash in today: data.
- Machine learning can deliver significant operational improvements in just about every division of your business.
Above article is an excerpt from the original article.