A process analytics software manufacturer will expand its efforts to integrate machine-learning algorithms into its applications. Seeq expects these improvements can help organizations incorporate data science investments — and their open source and third-party machine-learning algorithms — in their existing operations and allow access by operators and other front-line employees.
The company’s strategy for enabling machine-learning innovation provides end-user access to algorithms from a range of sources, Seeq says, instead of relying on a single machine-learning vendor or platform.
The Seeq initiative also address the challenge of scaling and deploying algorithms in manufacturing organization.
Examples of customers using applications to access and integrate data science include an oil-and-gas company deploying a deep-learning-based emissions prediction algorithm; a pharmaceutical company using an unsupervised learning algorithm to detect sensor drift in sensitive batch processes; and a chemical company using pattern learning to identify root causes of process instability and extend cycle time.