Seattle-based software company, Seeq, announced the release of R52 with new features to support the use of machine learning innovation in process manufacturing organisations. From oil and gas to FMCGs to pharmaceutical companies, Seeq’s analytic tools enable organisations to deploy their own or third-party machine learning algorithms into the advanced analytics applications used by front line process engineers and subject matter experts, thus scaling the efforts of a single data scientist to many front-line OT employees.
New Seeq features include add-on tools, display panes, and user-defined functions, each extending Seeq’s predictive, diagnostic, and descriptive analytics. The result is faster development and deployment of easy-to-use algorithms and visualisations for process engineers. R52 also allows end users to schedule Seeq Data Lab notebooks to run in the background to meet the top demands of their customers.
“Analytics software for manufacturing organisations is an area overdue for innovation,” says Steve Sliwa, CEO and Co-Founder of Seeq. “Spreadsheets replaced pen and paper 30 years ago for analytics and haven’t changed much since. By leveraging big data, machine learning and computer science innovations, Seeq is enabling a new generation of software-led insights.”
Seeq’s approach to integrating machine learning features in its applications addresses many of the reasons data science initiatives fail in manufacturing organisations.
Seeq first shipped easy-to-use machine learning-enabled features in Seeq Workbench in 2017, and introduced Seeq Data Lab in 2020 for access to Python scripts and arbitrary machine learning algorithms. With no code / low-code capabilities for process engineers and no scripting environment for data scientists engaged in functional engineering and data reduction efforts, this support for multiple audiences democratizes access to machine learning innovation.
Seeq’s approach to integrating machine learning capabilities into applications addresses many reasons why data science initiatives fail in manufacturing organisations.
• Seeq connects to all underlying data sources (historians, contexts, manufacturing applications, or other data sources) for data cleansing and modelling.
• Seeq supports a connected, two-way interaction between OT’s plant data and process engineering expertise and IT’s data science and algorithm expertise.
• Seeq provides a complete solution for algorithm development, algorithm updates and improvements over time, employee collaboration and knowledge gathering, and disclosure of insights for faster decision making.
In addition to Seeq Data Lab’s support for machine learning code and libraries, Seeq also enables access to Seeq / Python libraries through third-party machine learning solutions, including open source products such as Microsoft Azure Machine Learning, Amazon SageMaker, and Apache Anaconda. I will. For example, manufacturers using Amazon SageMaker use Seeq to evaluate machine learning insights to create work orders on SAP systems.
Seeq is available worldwide through a global partner network of system integrators that provide Seeq training and resale support in more than 40 countries, in addition to direct sales organisations in North America and Europe.
