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Building and Scaling Closed-Loop AI for Manufacturing Operations: Dr. Nikita Golovko - Software Architect for Industrial AI, Siemens
In theory, AI should learn, adapt, and improve continuously. But in reality, most deployments are static and disconnected from the evolving complexity of shop floor operations.
Most businesses lack tools to close the loop between:
⇨ Data collection
⇨ AI training
⇨ Deployment
⇨ Continuous retraining
⇨ Business impact validation
And they struggle to connect domain experts with data scientists.
To learn more about building and scaling closed-loop AI for industrial operations I recently sat down with Dr. Nikita Golovko who is a Software Architect for Industrial AI at Siemens.
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By Kudzai Manditereza
5
11 ratings
In theory, AI should learn, adapt, and improve continuously. But in reality, most deployments are static and disconnected from the evolving complexity of shop floor operations.
Most businesses lack tools to close the loop between:
⇨ Data collection
⇨ AI training
⇨ Deployment
⇨ Continuous retraining
⇨ Business impact validation
And they struggle to connect domain experts with data scientists.
To learn more about building and scaling closed-loop AI for industrial operations I recently sat down with Dr. Nikita Golovko who is a Software Architect for Industrial AI at Siemens.