Seeq Corporation, a leader in advanced analytics software for manufacturing and the Industrial Internet of Things, has added support for Microsoft Azure Machine Learning. This new Seeq Azure Add-on, which debuted at Microsoft Ignite 2021, a developer and IT professional conference hosted by Microsoft, enables process manufacturing organizations to install Azure Machine Learning models as Seeq Workbench Add-ons. As a result, IT departments’ machine learning algorithms and innovations can be operationalized, allowing frontline OT staff to increase productivity, sustainability indicators, and business outcomes.

Megan Buntain, Director of Cloud Partnerships at Seeq, said, “Seeq and Azure Machine Learning are critical and complementary solutions for a successful machine learning model lifecycle. By capitalizing on IT and OT users’ strengths, the Seeq Azure Add-on expands the Seeq experience and creates new opportunities for organizations to scale up model deployment and development.”

Customers of Seeq include businesses in the oil and gas, pharmaceutical, chemical, energy, mining, food and beverage, and other process industries. Insight Ventures, Saudi Aramco Energy Ventures, Altira Group, Chevron Technology Ventures, and Cisco Investments are among the investors in Seeq, which has raised over $100 million to far.

End users can acquire algorithms from a range of sources, including open-source, third-party, and internal data science teams, as part of Seeq’s machine learning innovation strategy. Data science teams may now use Azure Machine Learning Studio to construct models and then publish them using the Seeq Azure Add-ons functionality, which was just released on GitHub this week.

Frontline personnel with domain expertise may quickly access these models, evaluate them by overlaying near real-time operational data with model outputs, and provide input to the data science team using Seeq Workbench. This allows for an iterative set of interactions between IT and OT staff, speeding up time to insight for both groups while also establishing the continuous improvement loop required to keep machine learning activities running throughout their entire lifecycle.