Nvidia Blog 05月08日 01:15
NVIDIA’s Rama Akkiraju on How AI Platform Architects Help Bridge Business Vision and Technical Execution
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本文采访了NVIDIA的Rama Akkiraju,探讨了企业如何构建成功的AI基础设施。文章重点介绍了AI的发展趋势,从感知AI到生成式AI再到智能AI的加速演进,以及AI在软件开发中的重要性。Akkiraju强调了AI平台架构师在设计和构建满足特定业务需求的AI基础设施中的关键作用,并指出了AI基础设施的三个主要发展趋势:专用AI架构的集成、特定领域的模型和硬件的出现以及日益自主的智能系统。

💡AI的快速发展:文章指出AI技术的发展速度正在加快,从感知AI到生成式AI用了大约30年,而到智能AI的转变仅用了2年。Akkiraju认为,AI正在成为应用架构中不可或缺的一层,彻底改变了软件的编写方式。

🏢AI平台架构师的重要性:企业实施AI需要复杂的基础设施,包括数据摄取管道、矢量数据库、安全控制和评估框架。AI平台架构师作为战略业务愿景和技术执行之间的桥梁,在设计和构建AI基础设施方面起着关键作用。

🚀AI基础设施的三大趋势:Akkiraju预测了AI基础设施的三个主要趋势:一是专用AI架构集成到原生企业系统中;二是特定领域的模型和针对特定用例优化的硬件的出现;三是需要复杂内存和上下文管理的日益自主的智能系统。

Enterprises across industries are exploring AI to rethink problem-solving and redefine business processes. But making these ventures successful requires the right infrastructure, such as AI factories, which allow businesses to convert data into tokens and outcomes.

Rama Akkiraju, vice president of IT for AI and machine learning at NVIDIA, joined the AI Podcast to discuss how enterprises can build the right foundations for AI success.

Drawing on over two decades of experience in the field, Akkiraju provided her perspective on AI’s evolution, from perception AI to generative AI to agentic AI, which allows systems to reason, plan and act autonomously, as well as physical AI, which enables autonomous machines to act in the real world.

What’s striking, Akkiraju pointed out, is the acceleration in the technology’s evolution: the shift from perception to generative AI took about 30 years, but the leap to agentic AI happened in just two. She also emphasized that AI is transforming software development by becoming an integral layer in application architecture — not just a tool.

“Treat AI like a new layer in the development stack, which is fundamentally reshaping the way we write software,” she said.

Akkiraju also spoke about the critical role of AI platform architects in designing and building AI infrastructure based on specific business needs. Enterprise implementations require complex stacks including data ingestion pipelines, vector databases, security controls and evaluation frameworks — and platform architects serve as the bridge between strategic business vision and technical execution.

Looking ahead, Akkiraju identified three trends shaping the future of AI infrastructure: the integration of specialized AI architecture into native enterprise systems, the emergence of domain-specific models and hardware optimized for particular use cases, and increasingly autonomous agentic systems requiring sophisticated memory and context management.

Time Stamps

1:27 – How Akkiraju’s team builds enterprise AI platforms, chatbots and copilots.

4:49 – The accelerated evolution from perception AI to generative AI to agentic AI.

11:22 – The comprehensive stack required for implementing AI in enterprise settings.

29:53 – Three major trends shaping the future of AI infrastructure.

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