Lowering the Barrier: A Science Gateway for Scalable Machine Learning

We present a modular science gateway that simplifies the deployment of machine learning workflows across heterogeneous computing environments. Designed for domain researchers without DevOps expertise, our system lowers the barrier to scalable ML by integrating Clowder, an open-source data management platform, with Ray and Kubernetes for distributed execution. The platform supports data ingestion, visualization, and metadata management, with ML-specific UI enhancements. Workflows are executed via containerized extractors that interact with a shared Ray cluster. To demonstrate real-world utility, we apply our system to the detection of ice wedge polygons from Arctic satellite imagery. Researchers can fine-tune and run inference workflows through a simple web interface, without managing infrastructure. This approach enhances accessibility and reproducibility, and promotes the reuse of data and models across research communities.