Cloudera押注可移植性而非智能体作为企业AI优势
在 EVOLVE26 上，SiliconANGLE 企业版编辑 Paul Gillin 与 theCUBE 的 Rebecca Knight 讨论了 Cloudera 的企业 AI 战略。报道称，Cloudera 从 Hadoop 市场起步，后来构建了覆盖云端与本地基础设施的统一数据层，如今正将自身定位为“AI 随行云”。Gillin 表示，Cloudera 的智能体侧重数据与工作负载管理以及 GPU 容量优化，同时正将智能体工作流加入各项数据服务。他还称，开放权重模型在 token 使用中的占比已从两年前的约 20% 增至如今超过 80%。他指出，CIO 在采用 AI 自动化前会关注智能体如何受到控制、会留下哪些审计记录，以及其决策如何形成可供监管机构查阅的文件。
报道将跨本地部署、主权云及其他环境的可移植性列为 Cloudera 在企业 AI 领域的主要差异化优势。其智能体被描述为面向数据与工作负载管理、GPU 容量和成本控制的工具，而非通用助手；治理与可审计性则是采用过程中的关键问题。
Enterprises racing toward AI automation are finding that where their models and data run matters as much as what the models can do. Regulatory pressure, rising token costs and fear of lock-in are pushing companies to demand more control over their AI workloads.
That demand is shaping how data platform providers pitch their next act. Cloudera Inc. started out in the Hadoop market and later moved to a unified data layer that spans clouds and on-premises infrastructure. It is now positioning itself as an anywhere cloud for AI, according to Paul Gillin (pictured, right), enterprise editor at SiliconANGLE Media and theCUBE.
“I think big companies decided a long time ago. They didn’t want to be beholden to a single cloud provider and that’s sort of baked into the landscape now,” Gillin said. “I think what’s different about Cloudera is that they’re strong proponents of on-premises infrastructure, of sovereign clouds, of companies being able to run their workloads wherever they want and move from one to another.”
Gillin spoke with theCUBE’s Rebecca Knight (left) at EVOLVE26, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed AI automation and provided analysis of the event’s keynote speech. (* Disclosure below.)