Episode 1: The Critical Role of Industrial Grade Data Fabrics

Author photo: Colin Masson
ByColin Masson

The first in ARC’s Industrial AI Podcast Series kicks off with a conversation between Colin Masson, ARC Advisory Group’s Director of Research for Industrial AI, and Seeq Corporation Founder and CTO, Dustin Johnson as they discuss the critical role of Industrial Grade Data Fabrics.

Listen to Colin and Dustin set the stage for the Industrial AI (R)Evolution as they dispel some of the myths and new opportunities surrounding Generative AI, explore the long history of the application of machine learning and neural nets to Industrial AI use cases, and zero in on some of the complexities of contextualizing time series data that’s needed for many of those AI use cases. Dustin shares some examples of how Seeq has been applying AI techniques for more than 5 years and explores how Seeq is applying Generative AI to empower people and simplify processes to leverage the latest technology breakthroughs.

Tune in the podcast here.


Podcast topics and highlights:

  • Dispelling generative AI myths.

    • Generative AI has potential to improve industrial efficiency, but its not the right tool for every use case.

    • AI will probably not replace jobs and is more likely to empower people - but AI requires human and regulatory oversight.

    • Industrial organizations can't ignore the accessibility of AI tools, such as generative AI, and how it can solve real-world problems in manufacturing, including skills gaps.

  • AI in industrial manufacturing requires data validation, and fine-tuning models. 

    • Validating AI results in industrial settings is more crucial to prevent catastrophic consequences.

    • Contextualizing data is needed for industrial AI use cases, and there’s no avoiding the need for fine-tuning or retraining models on specific factory data.

  • The critical role of time series data in Industrial Data Fabrics. 

    • Much of the context required for training in Industrial AI use cases requires context to be added to time series data.

    • Seeq's strategy is to learn with the user as they use the tool, adapting to provide better service, rather than relying on a single configuration phase.

    • High-quality data is critical for mission critical industrial AI applications.

    • Working with time series data provides unique challenges that are not addressed by standard enterprise data fabrics  AI toolsets and require specialized tools, like Seeq's capsules for industrial grade data fabrics.

    • Seeq's shared how customers are leveraging these capabilities to make real-world impact with time series data.

  • Industrial AI applications already deliver business value, including predictive maintenance and process optimization. 

    • Seeq shared how their customers use machine learning to analyze data and identify process upsets, saving millions of dollars.

    • The most proven Industrial AI business value generators include predictive maintenance and quality use cases in industrial AI, with Seeq sharing examples including NOx compressor failure detection and sugar crystallization detection.

  • Industrial AI-powered automation and data analysis can help manufacture a more sustainable future.

    • Seeq's AI capabilities enhance decision-making for domain experts, leading to faster and more accurate analysis.

    • Empowering process engineers with AI and ML accelerates time to business value.

    • Modernizing industrial techniques and technologies, with the latest AI breakthroughs, is crucial for industrial organizations wanting to create a more sustainable future.

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