
Rojoniaina developed the Student AI/ML Platform Scaffold for the Ishangoai/AIMS_course repository, creating a modular workflow platform to support student experimentation and learning in AI and machine learning. The scaffold unified data ingestion, processing, analytics, and model tracking, enabling rapid prototyping and educational use. Rojoniaina implemented a Dagster-based data pipeline for ingestion, cleaning, and aggregation, and integrated MLflow for time-series forecasting with model tracking and promotion. The platform featured a FastAPI application mounting Gradio apps, including a Langchain-powered chatbot for interactive support. The work demonstrated depth in Python, data engineering, and model deployment, establishing a robust foundation for student projects.

October 2025: Delivered the Student AI/ML Platform Scaffold for Ishangoai/AIMS_course, establishing a modular end-to-end workflow platform that enables student-focused AI/ML experimentation and learning. The month focused on implementing a scaffold that unites data ingestion, processing, analytics, and model tracking, enabling faster experimentation and education outcomes. Key outcomes include a new folder structure, a Langchain/Gradio chatbot, a FastAPI application mounting Gradio apps, a Dagster-based data engineering pipeline (ingestion, cleaning, and aggregation), and an MLflow-integrated time-series forecasting pipeline with model tracking and promotion.
October 2025: Delivered the Student AI/ML Platform Scaffold for Ishangoai/AIMS_course, establishing a modular end-to-end workflow platform that enables student-focused AI/ML experimentation and learning. The month focused on implementing a scaffold that unites data ingestion, processing, analytics, and model tracking, enabling faster experimentation and education outcomes. Key outcomes include a new folder structure, a Langchain/Gradio chatbot, a FastAPI application mounting Gradio apps, a Dagster-based data engineering pipeline (ingestion, cleaning, and aggregation), and an MLflow-integrated time-series forecasting pipeline with model tracking and promotion.
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