Seeq Corporation, a leader in manufacturing and Industrial Internet of Things (IIoT) advanced analytics software, announces the expansion of its efforts to integrate machine learning (ML) algorithms into Seeq applications.

These improvements will 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 customers include companies in the oil & gas, pharmaceutical, chemical, energy, mining, food and beverage, and other process industries. Investors in Seeq, which has raised over $100 million to date, include Insight Ventures, Saudi Aramco Energy Ventures, Altira Group, Chevron Technology Ventures, and Cisco Investments.

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 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 performance-based continuous improvement workflows.

"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."