Cloudera赌注的可移植性，而不是agents，将在企业AI中将其分拆。
企业竞相实现AI自动化，发现他们的模型和数据运行的地方与模型所能做的一样重要。 监管压力、象征性成本上升和对锁定的恐惧促使公司要求更多地控制其AI工作量。
这一需求正在决定数据平台提供者如何进行下一步行动。 Cloudera Inc.始于Hadoop市场，后来转移到了覆盖云层和地上基础设施的统一数据层。 据SiliconANGLE Media Inc公司企业编辑Paul Gillin(图，右)说
“我认为大公司早就决定了。 Gillin 称表示，“我认为Cloudera的不同之处在于，他们强烈主张建立基础设施、主权云层、公司能够随时随地承担工作量、相互移动。”
吉林与CUBE的"丽贝卡骑士"(左)在EVOLVE26，在CUBE,SiliconANGLE Media的活流演播室独家播出时交谈。 他们讨论了AI自动化，并对活动的主题发言进行了分析。
主权AI和专用制造的 agents 驱动 AI 自动化
EVOLVE26主题发言中经常出现主权和私人AI。 调查指出，管制是企业希望将AI留在自己墙上的主要原因。 根据吉林的统计，开放量级模型给他们提供了一种实现的方法，从两年前的约20%的象征性使用量增长到今天的80%以上。 Cloudera还在其数据服务中增加了代理工作流程。 他解释说，通用agents更是一种消费现象。
“他们更像是办公工具，“管理工作量、优化GPU能力、真正涉及CIO的事物类型
仅仅拥有agent就不再是边缘了，因此Cloudera在它的长期多平台游戏上翻了一番。 吉林认为，它正在将想要的可移植性企业从云层扩展到他们的AI模型。 他注意到，CIOs在以AI自动化方式相信投出之前，会想知道agents是如何被控制的，他们留下了什么样的审计线索，以及agent决定如何被记录给监管者。
agents是一个非常大的问题，“那里还是一片狂野的西部
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Cloudera bets portability, not agents, will set it apart in enterprise AI
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 of SiliconANGLE Media Inc.
“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.
Sovereign AI and purpose-built agents drive AI automation
Sovereign and private AI came up frequently in the EVOLVE26 keynote. Surveys point to regulation as the main reason enterprises want to keep AI inside their own walls. Open-weight models give them a way to do it, having grown from about 20% of token use two years ago to more than 80% today, according to Gillin. Cloudera is also adding agentic workflows across its data services. General-purpose agents are more of a consumer phenomenon, he explained.
“They’re more of an office worker tool, but the Cloudera agents are tuned to managing data,” Gillin said. “To managing workloads, optimizing GPU capacity, the types of things that really concern CIOs, because they’re paying the bills for these very high training costs.”
Simply having an agent is no longer an edge, so Cloudera is doubling down on its long-standing multiplatform play. It is extending the portability enterprises wanted from their clouds to their AI models, according to Gillin. Before trusting that pitch with AI automation, CIOs will want to know how agents are controlled, what audit trails they leave behind and how agent decisions can be documented for regulators, he noted.
“This is a very big issue with agents right now,” Gillin said. “It’s still a wild west out there with agents.”
Stay tuned for the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of EVOLVE26:
Photo: SiliconANGLE
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