Seeq Announces Expanded Microsoft Azure Machine Learning Support

New Seeq Azure Add-on feature enables rapid deployment of Azure Machine Learning algorithms to frontline plant employees.

Seeq Corporation announced additional integration support for Microsoft Azure Machine Learning. This new Seeq Azure Add-on announced at Microsoft Ignite 2021, an annual conference for developers and IT professionals hosted by Microsoft, enables process manufacturing organizations to deploy machine learning models from Azure Machine Learning as Add-ons in Seeq Workbench. The result is machine learning algorithms and innovations developed by IT departments can be operationalized so frontline OT employees can enhance their decision-making and improve production, sustainability indicators, and business outcomes.

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 innovations provides end-users with access to algorithms from a variety of sources, including open-source, third-party, and internal data science teams. With the new Azure Machine Learning integration, data science teams can develop models using Azure Machine Learning Studio and then publish them using the Seeq Azure Add-ons feature, available this week on GitHub. Using Seeq Workbench, frontline employees with domain expertise can easily access these models, validate them by overlaying near real-time operational data with the model results and provide feedback to the data science team. This enables an iterative set of interactions between IT and OT employees, accelerating time to insight for both groups while creating the continuous improvement loop necessary to sustain the full lifecycle of machine learning operations.

“Seeq and Azure Machine Learning are critical and complementary solutions for a successful machine learning model lifecycle,” says Megan Buntain, Director of Cloud Partnerships at Seeq. “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.”

Along with increased access to machine learning models through this integration, Seeq’s self-service applications enable frontline employees to perform ad hoc analyses and use the models themselves, rather than rely on an IT team member for support. As the models yield results, Seeq empowers users to scale them across the organization to improve asset reliability, production monitoring, optimization, and sustainability.

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