AI-Powered Personalized Learning Platform on AWS
Author: Regis Benoit Brice Nde Tene
Status: UNVERIFIED (Score: 0/100)
Duration: 1 Month Capstone
Executive Summary
Developed an AI-powered personalized learning platform on AWS, leveraging LLMs to generate tailored learning paths and content recommendations. The platform incorporates a Kubernetes-managed microservices architecture and utilizes AI-assisted development tools to accelerate the development process, focusing on delivering a highly engaging and effective learning experience.
Key Skills
- Cloud-Native Architecture (AWS)
- Software Engineering
- Data Engineering
- Technical Communication
- Kubernetes
- AI System Evaluation Frameworks
- LLM Application Development
- Rapid Prototyping
- Cloud-Native Architecture (AWS)
- Software Engineering
- AI System Evaluation Frameworks
- Kubernetes
- AI-Assisted Development Tools
- Data Engineering
Project Execution Log
Stage 1: Design Cloud-Native Architecture and Data Model
This stage successfully laid the architectural groundwork for the AI-powered learning platform. It involved breaking down the system into manageable microservices, mapping them to appropriate AWS technologies, designing detailed data models for critical platform entities, and documenting all key design decisions. This robust foundation ensures future development can proceed efficiently, building on a scalable and well-thought-out cloud-native structure.
Deliverables
Stage 2: Develop LLM-Powered Content Recommendation Engine
This stage focused on building the intelligent core of the learning platform: an LLM-powered content recommendation engine. We established the development environment, integrated an LLM, meticulously crafted prompts to guide its output, and developed a robust API to serve personalized learning paths and content. The process included extensive testing and iterative refinement to ensure high-quality and relevant recommendations, significantly enhancing the platform's adaptive learning capabilities.
Deliverables
Stage 3: Implement Kubernetes-Managed Microservices on AWS
Deliverables
Stage 4: Build User Interface with AI-Assisted Tools
Deliverables
Stage 5: Conduct AI System Evaluation and Refine Models
Deliverables
Stage 6: Deploy, Test, and Present Platform Demo
Deliverables