Seeq's machine-learning support to democratize data-science innovation

Oct. 15, 2021
Initiative facilitates the integration of machine-learning algorithms from open source, third party, and customer data-science teams into Seeq applications.

Seeq Corporation announced the expansion of its efforts to integrate machine-learning algorithms into Seeq applications, which promises to enable organizations to operationalize their data-science investments, and their open-source and third-party machine learning algorithms for easy access by front-line employees.

Seeq’s strategy for enabling machine-learning innovation provides end user access to algorithms from a variety of sources, rather than forcing users to rely on a single machine learning vendor or platform. This addresses the diversity and types of algorithms available to organizations, including:

·       Open-sources algorithms and other public resources. For example, this week Seeq will publish two Seeq Add-ons to GitHub, including algorithms and workflows, for correlation and clustering analytics, which users can modify and improve based on their needs.

·       Customer-developed algorithms in Seeq Data Lab—or machine-learning operations platforms such as Microsoft Azure Machine Learning, Amazon SageMaker, Anaconda, and others—as part of data-science or digital-transformation initiatives.

·       Third-party algorithms provided by software vendors, partners, and academic institutions. AWS’s Lookout for Equipment, Microsoft Azure AutoML, BKO Services’ Pump Prediction, and Brigham Young University’s open-source offerings are examples of the emerging marketplace for industry and vertical market specific algorithms.

The Seeq initiative also claims to address the critical "last mile" challenge of scaling and deploying algorithms in manufacturing organization by putting data-science innovation in the hands of plant employees in easy-to-use applications: Seeq Workbench for advanced analytics, Organizer for publishing insights, and Seeq Data Lab for ad hoc Python scripting. 

“Data-science innovation in manufacturing organizations has the potential to deliver a step change in plant sustainability, productivity, and availability metrics,” says Kevin Prouty, VP industrials, IDC Corporation. “But to land this opportunity, companies must be able to deploy data-science innovation to frontline engineers with the expertise, data, and plant context to make decisions on insights provided by these new algorithms.” 

“Seeq provides a bridge between data science teams and their algorithms to front-line employees in hundreds of plants around the world,” says Brian Parsonnet, CTO at Seeq Corporation. “Deploying algorithms is now as simple as registering them in Seeq, and then defining which employees have access to each algorithm in their Seeq applications.”