Seeq Expands Machine Learning Support to Democratize Data Science Innovation

By Janice Abel

Company and Product News

Seeq Corporation announced the expansion of its efforts to integrate machine learning algorithms into Seeq applications. This will enable organizations to operationalize their data science investments, 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, 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 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.

This is in addition to Seeq support for the foundational elements of success with machine learning. This includes access to all manufacturing data sources—historian, contextual, and manufacturing applications—for data cleansing and modeling, support for employee collaboration and knowledge capture, quick iteration, and enabling performance-based continuous improvement workflows.

Examples of customers using Seeq applications to access and integrate data science innovation include an oil & gas company deploying a deep-learning-based emissions prediction algorithm, a pharmaceutical company using an unsupervised learning algorithm to proactively detect sensor drift in sensitive batch processes, and a chemical customer using pattern learning to identify root causes of process instability and extend cycle time.


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