Cloudera and Intel Collaborate to Advance Scalable Enterprise AI Adoption Across Industries
Cloudera, the only company bringing AI to data anywhere, announced
a collaboration with Intel Corporation, to advance enterprise-grade AI adoption
across industries throughout Asia Pacific (APAC). With the integration of
Intel Xeon 6 Processors with Cloudera’s AI platform, enterprises will
experience seamless implementation across hybrid and on-premises
environments, enabling them to deploy and manage advanced AI workloads
more efficiently, at scale.
"Enterprises
are no longer experimenting with AI. They're executing at scale, and that
demands a platform built for real-world complexity,” said Remus Lim, Senior
Vice President of Asia Pacific and Japan at Cloudera. “Cloudera AI brings
intelligence to data, backed by enterprise-grade security, governance, and flexibility.
Our deep collaboration with Intel helps to ensure that our customers in India
and across APAC have the ability to turn AI innovation into tangible business
outcomes.”
“AI
is transforming how businesses operate, while its success depends on efficient
and responsible deployment. We believe that establishing an AI-native
enterprise demands a new class of platforms built for scale, resilience, and
cost-efficiency,” said Santhosh Viswanathan, Vice President and Managing
Director, India Region, Intel. “As enterprises begin to accelerate their
AI deployments, the Cloudera stack powered by Intel’s
enterprise-ready AI platforms helps customers unlock value from their data
faster, more securely, and at scale.”
A
whitepaper co-released by Cloudera and Intel highlights how the
collaboration in delivering cutting-edge innovations is shaping the future of
enterprise AI, including:
· Agentic AI and Human-in-the-Loop systems
· Conversational AI powered by
Automatic Speech Recognition and Named Entity Recognition on-premises
· Model Context Protocol, which bridges data
marts with LLMs for seamless and secure access
· Small Language Models deliver powerful
AI capabilities in compact, efficient frameworks
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