(MENAFN- Mepax) Seeq Corporation, a leader in manufacturing and Industrial Internet of Things advanced analytics software,
announced today 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.
In addition to launching the Azure integration, Seeq is also expanding its list of published open source
algorithms this week with the addition of a new Seeq Add-on to GitHub for multivariate pattern search.
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Seeq’s open source gallery also includes algorithms and workflows for correlation and clustering analytics,
which users can modify and improve based on their own needs.
Seeq has been available as a SaaS application in the Azure Marketplace since 2019, with support for many
Azure cloud services including Synapse, Azure Data Lake, and Active Directory. Seeq also supports
connectivity to Azure Data Explorer, Time Series Insights, and Power BI.
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 seeq.com.
About Seeq Corporation
Founded in 2013, Seeq publishes software applications for manufacturing organizations to rapidly find and
share data insights. Oil & gas, pharmaceutical, specialty chemical, utility, renewable energy and numerous
other vertical industries rely on Seeq to improve production outcomes, including yield, margins, quality, and
safety. Headquartered in Seattle, Seeq is a privately held virtual company with employees across the United
States and sales repr
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