Cloudera 扩展其AI生态系统有伙伴关系和包装蓝图
Cloudera Inc.拥有其企业人工智能生态系统的宣布扩展，引入了由Nvidia公司技术所构建的预配置AI应用，并拓展了与Neo4j Inc.和Mistral AI SAS的伙伴关系。
Cloudera 称倡议旨在帮助企业将AI项目投入生产，同时保持对其数据、基础设施和部署成本的控制。
这些提议加强了Cloudera将AI处理带入企业数据而不是要求各组织将敏感信息移入集中云环境的战略。
称 Wim Stoop是Cloudera的产品营销高级主管，在会议上接受采访。
公告的核心是Cloudera Blueprints，即时部署的AI包组合，将Nvidia的AI参考工作流程与Cloudera的数据管理，安全和治理能力结合起来。
蓝图采用Nvidia Agent工具包并被部署在Cloudera AI开发平台上，旨在减少将实验AI应用转化为生产系统的整合工作。
最初的组合包括金融交易分析、自动临床文件、电信网络配置和合成视频检测等应用。 它们包含各种技术，包括Nvidia NIM微服务，Nemotron模型和图形处理单元加速数据处理。
Stoop 称企业日益了解AI的潜力，但与部署的业务和财务影响相抗衡。
“许多组织面临的最大挑战似乎是如何从试点到实际在生产中运作而又不造成可怕成本的东西。”
Cloudera 也称宣布
Neo4j的知识图提供了语义上下文，可以帮助agents连接关于客户，交易，产品和业务流程的信息。 该技术与Cloudera管理的数据基础设施相结合，旨在支持能够解释企业信息和采取行动的AI系统。
扩大后的生态系统还包括与Mistral AI和Vast Data Inc.的伙伴关系，为客户提供了AI模型和基础设施的额外选项。
Cloudera高管称 Mistral关系对其主权AI策略特别重要，它强调客户对数据，模型和基础设施的控制。
各组织越来越想在自己的环境中灵活地使用开放式模型，而不是完全依靠外部托管的专有系统，Stoop 称
“我们与Mistral的伙伴关系就是这方面的一个很好的例子”，称引述了在当地部署模型并用专有企业信息定制模型的能力。
这种灵活性在欧洲很重要，因为欧洲的数据隐私要求、地缘政治关切和条例正在促使公司审查人工智能工作量在何处以及由谁控制工作量。
然而，Stoop告诫说，主权AI超越了将数据保存在国界之内。 各组织还必须建立对模式执行、数据线条、基础设施和业务过程的控制。
Cloudera 称其方法保留了跨公共云层，私人云层，地上基础设施和边缘环境的部署灵活性，使得客户可以选择符合其要求的配置。
Stoop 称灵活性还可以帮助各组织管理AI推论的经济学，其中计算成本可以随着应用规模的扩大而大幅增长。 目标不是把每个工作量都移入主权环境
称认为“各组织必须具有这种灵活性
Cloudera相信，将可重复使用的AI应用、更广泛的技术伙伴关系和整个环境的一致治理结合起来，将有助于企业克服将AI试验与生产分开的技术和经济障碍。
照片来自Facebook上的Cloudera
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Cloudera expands its AI ecosystem with partnerships and packaged blueprints
Cloudera Inc. has announced an expansion of its enterprise artificial intelligence ecosystem, introducing preconfigured AI applications built with Nvidia Corp. technology and expanding partnerships with Neo4j Inc. and Mistral AI SAS.
Cloudera said the initiatives are intended to help enterprises move AI projects into production while maintaining control over their data, infrastructure and deployment costs.
Announced at the company’s Evolve26 conference in New York, the offerings reinforce Cloudera’s strategy of bringing AI processing to enterprise data rather than requiring organizations to move sensitive information into centralized cloud environments.
“We’ll bring AI to where the data is and where the data lives,” said Wim Stoop, Cloudera’s senior director of product marketing, in an interview at the conference.
The centerpiece of the announcements is Cloudera Blueprints, a portfolio of ready-to-deploy AI packages that combine Nvidia’s AI reference workflows with Cloudera’s data management, security and governance capabilities.
Built using Nvidia Agent Toolkit and deployed through the Cloudera AI development platform, the blueprints are intended to reduce the integration work involved in turning experimental AI applications into production systems.
The initial portfolio includes applications for financial transaction analysis, automated clinical documentation, telecommunications network configuration and synthetic video detection. They incorporate technologies including Nvidia NIM microservices, Nemotron models and graphics processing unit-accelerated data processing.
Stoop said enterprises increasingly understand AI’s potential but struggle with the operational and financial implications of deployment.
“The biggest challenge for a lot of organizations seems to be how to go from a pilot to something that actually operates in production without incurring horrific costs,” he said.
Cloudera also announced an expansion of its Enterprise AI Ecosystem, adding Neo4j’s graph technology to help AI agents understand relationships among enterprise data.
Neo4j’s knowledge graphs provide semantic context that can help agents connect information about customers, transactions, products and business processes. Combined with Cloudera’s governed data infrastructure, the technology is intended to support AI systems that can reason about enterprise information and take actions.
The expanded ecosystem also includes partnerships with Mistral AI and Vast Data Inc., giving customers additional options for AI models and infrastructure.
Cloudera executives said the Mistral relationship is particularly important to its sovereign AI strategy, which emphasizes customer control over data, models and infrastructure.
Organizations increasingly want the flexibility to use open-weight models within their own environments instead of relying exclusively on externally hosted proprietary systems, Stoop said
“Our partnership with Mistral is a great example of that,” he said, citing the ability to deploy models locally and customize them with proprietary enterprise information.
That flexibility matters in Europe, where data privacy requirements, geopolitical concerns and regulations are pushing companies to examine where AI workloads run and who controls them.
However, Stoop cautioned that sovereign AI goes beyond keeping data within national boundaries. Organizations must also establish control over model execution, data lineage, infrastructure and operational processes.
Cloudera said its approach preserves deployment flexibility across public clouds, private clouds, on-premises infrastructure and edge environments, allowing customers to choose configurations that match their requirements.
Stoop said flexibility could also help organizations manage the economics of AI inference, where computing costs can grow substantially as applications scale. The goal isn’t to move every workload into a sovereign environment, but to let organizations choose the most appropriate deployment model, he said.
“It’s important for organizations to have that flexibility to be able to choose the right infrastructure for the right job,” he said.
Cloudera is betting that combining reusable AI applications, broader technology partnerships and consistent governance across environments will help enterprises overcome the technical and economic barriers separating AI experimentation from production.
Photo: Cloudera on Facebook
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