The article explores the rapid evolution of AI models, moving beyond the familiar large language models (LLMs) towards a more diverse landscape of xLMs. These xLMs will encompass various sizes, domains, and capabilities, demanding a shift in data infrastructure to support their needs. The author highlights the challenges and opportunities presented by this evolution, emphasizing the importance of live AI and real-time data pipelines.
AI moves fast. A couple of years ago, the IT trade held perceptions of the “fanciful” artificial intelligence functions that we might one day start to enjoy, if the IT industry ever finally perfected automation technologies. During this previous period of innocence, few people talked about language models and retrieval augmented generation was but a glint in the eye of the AI innovators who would rapidly escalate us into the agentic AI tool era of this year’s hype cycle.
California Storm: Evacuation Warnings Issued In Wildfire-Hit Los Angeles Areas Amid Mudslide And Debris Flow Fears Undertaking frequent batch uploads while maintaining meticulous attention to data accuracy demands a team of experts with specialist skills, frequently leading to cost and resource barriers. Growing demand for applications with to-the-moment accuracy exacerbates this challenge.
AI Artificial Intelligence Xlm Foundation Models Live AI Data Infrastructure Data Engineering
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