• Leaders: Wilson Wang Tina Tsou 
• Objective: To design, develop, and deploy a robust Agent-as-a-Service (AaaS) platform leveraging edge computing to run AI models locally on edge devices. This platform aims to enhance performance, reduce latency, and improve scalability by deploying machine learning agents closer to data sources and end-users, ensuring efficient and real-time processing of AI tasks.
• Approach: The approach for Edge AaaS (Agent-as-a-Service) involves designing and implementing a scalable platform that leverages edge computing to deploy and manage AI models on edge devices, ensuring real-time processing, reduced latency, and enhanced performance by conducting thorough requirements analysis, robust architecture design, seamless integration, and continuous monitoring and optimization.

Introduction

The Edge AaaS (Edge Agent-as-a-Service) project aims to deploy AI agents on edge devices using a Function-as-a-Service (FaaS) system. This approach will accelerate user access to AI models by leveraging edge computing capabilities.

Objectives

• Deploy AI agents on edge devices to provide fast and efficient access to AI models.

• Utilize FaaS to manage and scale AI services dynamically.

Scope

Breakdown

TBD

Project Timeline

TBD

Resource Allocation

TBD

Risk Management

TBD

Communication Plan

TBD

Quality Assurance

TBD

Documents

TBD