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Optimization of AI Models Deployments in the cloud-edge continuum

Service description

We offer solutions to optimize the deployment and performance of AI models across the cloud-edge continuum. This service ensures efficient resource utilization, reduces latency, and enhances scalability by seamlessly integrating AI models for real-time processing at both the cloud and edge levels.
Expected results: Improved model performance, reduced latency, better resource utilization, enhanced scalability, and seamless integration across cloud and edge environments for real-time AI processing.
Methodology: Assessment: Review the current AI model deployment strategy across cloud and edge environments, identifying inefficiencies and areas for optimization.
Needs Assessment: Evaluate the business requirements for AI model performance and deployment in cloud and edge environments.
Model Training: Train AI models in a centralized cloud environment.
Edge Deployment: Deploy optimized AI models to edge devices for real-time, low-latency processing.
Cloud Integration: Ensure seamless integration between cloud and edge systems for continuous data flow and model updates.
Performance Monitoring: Continuously monitor performance across the continuum and optimize model efficiency based on real-time data and usage patterns.
Target: Manufacturing & Automotive
  • Organisation: REPLY
  • Type: Experiment
  • SUT/E: Predictive AI models
  • Average duration: 4.0 -- 6.0 months
  • Contact: m.diflorio@reply.it

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Co-Funded by the European Union Under grant agreement number 101100707

Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the Directorate-General for Communications Networks, Content and Technology. Neither the European Union nor the granting authority can be held responsible for them.

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