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Optimization of AI Models Deployments in the cloud-edge continuum

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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: 4-6 months
  • Contact: m.diflorio@reply.it

Improving production with AI technologies