Developed an end-to-end student data engineering and machine learning platform for the Ishangoai/AIMS_course repository, focusing on scalable onboarding and reproducible workflows. The work introduced a new folder structure supporting agents, APIs, and Dagster pipelines, enabling streamlined data engineering and ML experimentation. Leveraging Python and Docker, a FastAPI application was integrated with Gradio to provide interactive demos, while a basic chatbot with Google Search capabilities was implemented for enhanced user interaction. Additionally, a comprehensive pipeline for ERA5 temperature data analysis and model deployment was established, supporting rapid experimentation and demonstration of machine learning concepts within a robust, modular framework.