AI-SPRINT defines a novel framework for the design and operation of AI applications in computing continua.

AI-SPRINT goes beyond supporting AI applications development by enabling the seamless design and partition of AI applications among the plethora of cloud-based solutions and AI-based sensor devices, providing security and privacy guarantees.

Use Cases

AI-Sprint - Unleashing the potential of Artificial Intelligence and Edge Computing in three thematic use cases

AI-SPRINT Goals

Creating new tools for the development of AI applications whose components will run seamlessly and securely across distributed heterogeneous infrastructures.
Providing advanced strategies to design and optimally partition AI models considering model accuracy, application performance, security and privacy constraints.
Delivering solutions for the agile delivery and secure automatic deployment and execution of AI applications and models across the cloud-edge continuum while preserving the privacy of users’ data.
Implementing a runtime environment to monitor application executions with data load variations of sensor streams or component failures.
Supporting continuous training and application architecture enhancement to add fresh data to AI applications, exploiting novel edge AI-based sensor capabilities.
Driving innovation

AI-SPRINT comes into play as a framework for developing AI applications.

The new EU research initiative set to drive innovation for Artificial Intelligence applications in cloud and edge environments.