Enhancing Peer-to-Peer Rental Transactions for Gluu.Repair

Gluu.Repair is transitioning from Version 1 to Version 2, aiming to enhance its Peer-to-Peer Rental Feature. The current system allows users to share specific collections with clients, but lacks a streamlined process for clients to select items, choose rental durations, and view the total value of their rental requests. The project focuses on designing a comprehensive transaction flow that addresses these gaps. Key tasks include developing a user-friendly interface for clients to select items and rental periods, integrating a system to display the total rental value, and creating a seamless approval and payment capture process. The goal is to ensure a smooth and efficient rental experience for both users and clients, aligning with Gluu.Repair's organizational change objectives.

Image Recognition Model Enhancement for Condition Reporting

The project focuses on enhancing Gluu's multi-modal image recognition model to improve the accuracy and efficiency of condition reporting for client products. The goal is to develop a custom Convolutional Neural Network (CNN) and integrate You Only Look Once (YOLO) object detection techniques to better identify and classify product conditions. This project will allow learners to apply their knowledge of machine learning, computer vision, and neural networks to a real-world application. By focusing on CNN and YOLO, the project aims to create a robust system capable of handling diverse product images and providing precise condition assessments. The project will involve tasks such as data preprocessing, model training, and performance evaluation, all of which are crucial for developing a reliable image recognition system.

AI Governance and Security Framework for Gluu.Repair

Gluu.Repair is committed to promoting sustainability and responsible consumer choices through the repair and repurposing of luxury items. As the company integrates AI into its operations, establishing a robust governance and security framework is essential to ensure the ethical and secure use of AI technologies. This project aims to develop a foundational layer of governance and security for Gluu.Repair's AI application. The project will involve researching best practices in AI governance, identifying potential security vulnerabilities, and proposing a comprehensive framework that aligns with industry standards. The goal is to create a secure and transparent AI environment that supports Gluu.Repair's mission while safeguarding user data and maintaining trust. - Research AI governance best practices and security protocols. - Identify potential security vulnerabilities in the current AI application. - Propose a governance and security framework tailored to Gluu.Repair's needs. - Ensure alignment with industry standards and ethical guidelines.

Integrating AI and Cloud Infrastructure for Enhanced Asset Management

Gluu is in the process of enhancing its technological infrastructure to streamline operations and improve asset management. The project involves updating an Amazon EC2 instance to support the integration of advanced AI models and cloud services. The primary goal is to integrate the Dify Bot into Supabase for efficient data management and Vercel for a responsive front-end interface. This integration will facilitate real-time learning and condition reporting for Gluu's real-world assets, leveraging a Multi-Modal AI model. The project aims to provide learners with hands-on experience in cloud computing, AI integration, and front-end development, applying their classroom knowledge to solve real-world problems. Key tasks include configuring the EC2 instance, setting up the Dify Bot, and ensuring seamless communication between Supabase and Vercel.