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The adaptation of machine learning (ML) in scientific and medical research in recent years has heralded a new era of innovation, catalyzing breakthroughs that were once deemed unattainable. In this paper, we present the Machine Learning Hub (ML Hub) – a web application offering a single point of access to pre-trained ML models and datasets, catering to users across varying expertise levels. Built upon the NSF-funded Tapis v3 Application Programming Interface (API) and Tapis User Interface (TapisUI), the platform offers a user-friendly interface for model discovery, dataset exploration, and inference server deployment. 

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Large scale collaborative science requires tools to facilitate sharing of data, protocols, analysis tools, as well as data products and their provenance. We describe here two recent science gateways successfully deployed to accomplish collaborative research. The first is the Synergistic Discovery and Design Environment (SD2E), which was a web-based analysis platform for collaborative analysis, data sharing, and application development. The second is the 3D Electron Microscopy (3DEM) portal, which is a web-based research platform focused on developing and disseminating new technologies for enhanced resolution 3DEM. Both gateways were hosted and connected to high-performance computing resources at the Texas Advanced Computing Center. 

Containerized applications have exploded in popularity in recent years, due to their ease of deployment, reproducible nature, and speed of startup. Accordingly, container orchestration tools such as Kubernetes have emerged as resource providers and users alike try to organize and scale their work across clusters of systems. This paper documents some real-world experiences of building, operating, and using self-hosted Kubernetes Linux clusters. It aims at comparisons between Kubernetes and single-node container solutions and traditional multi-user, batch queue Linux clusters. The authors of this paper have background experience first running traditional HPC Linux clusters and queuing systems like Slurm, and later virtual machines using technologies such as Openstack. Much of the experience and perspective below is informed by this perspective. We will also provide a use-case from a researcher who deployed on Kubernetes without being as opinionated about other potential choices.