Seeq Expands Machine Learning Support to Democratize Data Science Innovation

Oct. 14, 2021
New initiative facilitates the integration of machine learning algorithms from open source, third party, and customer data science teams into Seeq applications.

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 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 $100M 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.”

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 company using pattern learning to identify root causes of process instability and extend cycle time.

“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.”

Seeq first shipped machine learning features in 2017 in Seeq Workbench, and then in 2020 introduced Seeq Data Lab for Python scripting and access to any machine learning algorithm. This support for multiple audiences—with point-and-click features for process engineers, low code scripting, and a programming environment for data scientists engaged in feature engineering and data reduction efforts—delivers an end-to-end solution for organizations with all levels of analytics sophistication.

Seeq is available worldwide through a global partner network of system integrators, which provides training and resale support for Seeq in over 40 countries, in addition to its direct sales organization in North America and Europe. 

To learn more about Seeq visit

Sponsored Recommendations

Put the Plant Floor in Your Pocket with Ignition Perspective

Build mobile-responsive HTML applications that run natively on any screen.

Ignition: Industrial-Strength System Security and Stability

Ignition is built on a solid, unified architecture and proven, industrial-grade security technology, which is why industrial organizations all over the world have been trusting...

Iron Foundry Gains Competitive Edge & Increases Efficiency with Innovative Technology

With help from Artek, Ferroloy implemented Ignition to digitally transform their disconnected foundry through efficient data collection and analysis while integrating the new ...

Empowering Data Center Growth: Leveraging Ignition for Scalability and Efficiency

Data center growth has exploded over the past decade. Initially driven by organizations moving their computer assets to the cloud, this trend has only accelerated. With the rise...