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While artificial intelligence and machine learning (AI/ML) frameworks gain prominence in science and engineering, most researchers face significant challenges in adopting complex AI/ML workflows to campus and national cyberinfrastructure (CI) environments. Data from the Texas A&M High Performance Computing (HPRC) researcher training program indicate that researchers increasingly want to learn how to migrate and work with their pre-existing AI/ML frameworks on large scale computing environments. Building on the continuing success of our work in developing innovative pedagogical approaches for CI- training approaches, we expand CI-infused pedagogical approaches to teach technology-based AI and data sciences. We revisit the pedagogical approaches used in the decades-old tradition of laboratories in the Physical Sciences that taught concepts via experiential learning. Here, we structure a series of exercises on interactive computing environments that give researchers immediate hands-on experience in AI/ML and data science technologies that they will use as they work on larger CI resources. These exercises, called “tech-labs,” assume that participating researchers are familiar with AI/ML approaches and focus on hands-on exercises that teach researchers how to use these approaches on large-scale CI. The tech-labs offer four consecutive sessions, each introducing a learner to specific technologies offered in CI environments for AI/ML and data workflows. We report on our tech-lab offered for Python-based AI/ML approaches during which learners are introduced to Jupyter Notebooks followed by exercises using Pandas, Matplotlib, Scikit-learn, and Keras. The program includes a series of enhancements such as container support and easy launch of virtual environments in our Web-based computing interface. The approach is scalable to programs using a command line interface (CLI) as well. In all, the program offers a shift in focus from teaching AI/ML toward increasing adoption of AI/ML in large-scale CI.
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This content will become publicly available on July 17, 2025
Building a Cybertraining program for Climate Scientists in the Pacific to integrate Cyberinfrastructure and Open Science
The Cyberinfrastructure Training and Capacity Building in Climate and Environmental Sciences (CI-TRACS) program represents a pioneering initiative aimed at enhancing cyberinfrastructure proficiency within Hawaii’s academic community. This paper outlines the program’s comprehensive strategy, which integrates curriculum development, hands-on workshops, and professional growth opportunities to cultivate a robust foundation in CI practices. The initiative’s core objective is to elevate CI literacy, promote cross-disciplinary cooperation, and endorse the principles of open science. Significant contributions from the CI-TRACS program include a suite of educational materials and resources tailored for integration into higher education syllabi. Collaboration with the Hawaii Data Science Institute has been instrumental in nurturing a burgeoning network of data science professionals. The CI-TRACS program is instrumental in realizing the shared vision of equipping Hawaii’s emerging workforce with the sophisticated CI skills necessary to navigate and excel in the evolving landscape of climate and environmental sciences.
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- PAR ID:
- 10545441
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400704192
- Page Range / eLocation ID:
- 1 to 4
- Subject(s) / Keyword(s):
- Cyberinfrastructure Climate Science Data Science
- Format(s):
- Medium: X
- Location:
- Providence RI USA
- Sponsoring Org:
- National Science Foundation
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