Creating predictive models is complex and time-consuming. ML Core automates this with AutoML, offering a simple interface for quick, accurate real-time data predictions.
Goals
Simplify the creation of predictive models using automated machine learning workflows.
Deliver accurate real-time data predictions through an intuitive operator interface.
Clarify the primary objective described in the source.
Align the initiative with stated business priorities.
Challenge
Industrial datasets from fragmented sources lack the structure needed for efficient model
Generic machine learning models fail to capture the unique operational behaviors of industrial
Address the key constraints noted in the source.
Reduce manual effort across the current process.
Solutions
Deploy template-driven contextualization to streamline asset analytics and ensure consistency
Embed predictive models directly into existing platforms like Seeq and AVEVA PI for real-time
Tune machine learning models to the plant's unique characteristics and historical data
Apply the described approach to the core problem.
Results
Operators receive early predictive alerts that allow for proactive interventions and reduced
Integrated dashboards provide immediate visibility into model outputs to enhance
Plant operations achieve streamlined asset analytics through consistent, easy-to-use templates.
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