A new service from Unisys is not only designed to help clients predict future business conditions, but also recommends actions to take in response to change.
Unisys Corporation has launched its Machine-Learning-as-a-Service, a new offering that enables organisations to predict changes in business conditions and quickly evaluate potential responses to those changes.
Available as part of the Unisys Analytics Platform, the new service combines a library of machine learning algorithms with a suite of proven methodologies and processes that analyse and extract important details and insights from clients’ data to generate prescriptive reporting – analytics that forecast future business and operating conditions for clients, but also recommend future actions to facilitate and support decision-making.
“Unisys Machine-Learning-as-a-Service not only facilitates predictive analytics, it helps organisations transform and optimise their business processes. By providing an unmatched combination of skilled data scientists and vertical domain expertise, Unisys can offer unique insight into our clients’ business and how analytics can best transform them to new levels of success,” said Dr. Rod Fontecilla, vice president and global lead for analytics at Unisys.
“With our new Machine-Learning-as-a-Service offering, we can give our clients a scalable platform running in the cloud or on premise to drive business insights and mission effectiveness.”
Machine learning is a type of artificial intelligence that enables computers to constantly optimise and improve without human intervention. Using algorithms that iteratively draw lessons from data, machine learning systems identify patterns and find hidden insights without being explicitly programmed.
This ability to apply complex calculations dynamically and repeatedly on large data sets allows organisations to assess current conditions, as well as anticipate future circumstances and strategically position themselves accordingly.
Unisys Machine-Learning-as-a-Service leverages agile development principles to help clients understand and work with their data. Engagements begin with a proof-of-concept phase in which Unisys data scientists partner with the client’s subject matter experts to identify available data and get an overall understanding of their business needs and objectives. From this close collaboration, Unisys implements machine learning algorithms to analyse the client’s data and then develop advanced data analytics models for predicting outcomes.
Following the successful proof-of-concept, Unisys and the client move to the production phase and continue the agile engagement process to expand and formalise the analytical framework, while leveraging additional data and predictive models and continuing to monitor and refine the accuracy and integrity of the models and their results.
“This dynamic and iterative process helps clients use early insights to drive new questions and make new predictions from their data with very little investment on their part,” Fontecilla said. “This provides a quick and reliable way for clients to experiment before making significant investments.”
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