DGIT and the UCLA Library have come together to discuss reproducibility, data management and publishing strategies for biomedical researchers.
This month’s topic is: Data-centric AI Brown Bag: AI Reproducibility, AI Readiness, and FAIR
Amidst the backdrop of powerful AI engines producing essays and pirate shanties, it’s easy to forget that machine learning (ML) is still an onerous process for many that begins with finding and cleaning data. AI/ML relies on machine actionable data and adds complexity to an already difficult task for data stewards, research computing facilitators, and researchers. This session will introduce the work of the FAIR and ML, AI Readiness, and AI Reproducibility (FARR) Research Coordination Network (RCN). No prior understanding of AI/ML is needed. Please bring your lunch to our virtual session along with questions, challenges, or pain points as it relates to any of the topics.