Alhamdulillah for showcasing the deployment of my Bachelor Project at the German University in Cairo: an Adaptive Computational Thinking Education Platform powered by Machine Learning. The core objective was to engineer a platform that doesn't just serve educational content, but adapts to how a student learns and performs in real-time. To achieve this, I bridged ML with a containerized full-stack architecture. Here is a technical breakdown of the system: Core ML Capabilities * Dynamic Learning Style Adaptation: Engineered Bayesian Networks utilizing pgmpy to continuously infer and update a student's preference between verbal and visual learning. * Adaptive Exercise Difficulty: Implemented a Bayesian model that dynamically serves easy, medium, or hard exercises based on real-time solving time taken. * Struggling Detection: Integrated an LSTM model that monitors coding attempts to provide a guide when a student is supposedly detected to be struggling. System Architecture * Node.js Orchestrator: Serves as the central hub for business logic, database operations, secure user authentication. * Python FastAPI Server: Dedicated to hosting the ML services and managing secure, isolated code execution environments. * React Client: Provides a clean, interactive UI for lectures and active coding exercises. Cloud & DevOps Infrastructure * Azure Container Registry (ACR): Acts as the centralized hub for the deployment pipeline, securely managing the Docker images for both backend services. * Azure Container Apps: Hosts the Node.js orchestrator as a dedicated single instance. * DigitalOcean Droplets: Hosts the FastAPI server, specifically utilizing a Docker-out-of-Docker architecture to safely spawn and isolate containers strictly for executing user-submitted code. #SoftwareEngineering #MachineLearning #FullStack #DevOps #React #Nodejs #Python #FastAPI #SystemArchitecture #GermanUniversityInCairo #GUC