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(This message was added in version 6.7.0.) in /home/mother99/jacksonholdingcompany.com/wp-includes/functions.php on line 6114The hype triggered by the emergence of generative artificial intelligence (AI) feels a lot like the early days of cloud, bringing the topic\u2014and the need for a strategy\u2014to the front of the IT leader ag\u2026 Read more on Cisco Blogs<\/a><\/p>\n \u200b<\/p>\n The hype triggered by the emergence of generative artificial intelligence (AI) feels a lot like the early days of cloud, bringing the topic\u2014and the need for a strategy\u2014to the front of the IT leader agenda.<\/p>\n But while AI is poised to change every aspect of our lives, the complexity of AI infrastructure and operations is holding things back. At Cisco, we believe AI can be a lot easier when we find ways to avoid creating islands of operations and bring these workloads into the mainstream.<\/p>\n AI workloads place new demands on networks, storage, and computing. Networks need to handle masses of data in motion to fuel model training and tuning. Storage needs to scale effortlessly and be closely coupled with compute. Plus, computing needs to be accelerated in an efficient way because AI is seeping into every application.<\/p>\n Consider video conferencing. In addition to the familiar CPU-powered elements like chat, screen sharing, and recording, we now see GPU-accelerated components like AI inference for real-time transcription and generative AI for meeting minutes and actions. It\u2019s now a mixed workload. More broadly, the demands of data ingest and preparation, model training, tuning, and inference all require different intensities of GPU acceleration.<\/p>\n IT teams are being asked to stand up and harden new infrastructure for AI, but they do not need new islands of operations and infrastructure or the complexity that comes with them. Customers with long-standing operating models built on solutions like FlexPod and FlashStack can bring AI workloads into that same domain of simplicity, scalability, security, and control.<\/p>\n The constituent technologies in these solutions are ideal for the task:<\/p>\n UCS X-Series Modular System with X-Fabric technology allows for flexible CPU\/GPU ratios and cloud-based management for computing distributed anywhere across core and edge. We\u2019re working hard with our ecosystem partners to pave a path for customers to mainstream AI. I\u2019m pleased to announce an expanded road map of Cisco Validated Solutions on proven industry platforms, along with new automation playbooks for common AI models.<\/p>\n These solutions span virtualized and containerized environments, multiple converged and hyperconverged infrastructure options, and important platforms like NVIDIA AI Enterprise (NVAIE).<\/p>\n <\/a>Integrated, validated solutions on proven platforms.<\/strong><\/em><\/p>\n These solution frameworks rely on our three-part approach:<\/p>\n Mainstreaming<\/strong> AI infrastructure to reduce complexity across core, cloud, and edge. \u201cBuilding on a decade of collaboration, Cisco and Red Hat are working together to help organizations realize the value of AI through improved operational efficiencies, increased\u00a0 productivity and faster time to market. Cisco\u2019s AI-focused Cisco Validated Design can help simplify, accelerate and scale AI deployments using Red Hat OpenShift AI to provide data scientists with the ability to quickly develop, test and deploy models across the hybrid cloud.\u201d <\/em>\u2014Steven Huels, Senior Director and General Manager, Artificial Intelligence Business, Red Hat.<\/em><\/strong><\/p>\n AI\u2019s infusion into every industry and application will continue to accelerate, even as the component technologies each make their way through the hype cycle to adoption. Increased data collection and computing power, advancements in AI frameworks and tooling, and the generative AI revolution\u2014are all fueling change. Let us help you build on trusted architectures and take these workloads mainstream for maximum effect.<\/p>\n \u00a0\u00a0AI workloads place new demands on networks, storage, and computing. Networks need to handle masses of data in motion to fuel model training and tuning. Storage needs to be closely coupled with compute in order to scale effortlessly. Cisco can help you build on trusted architectures for maximum effect.\u00a0\u00a0Read More<\/a>\u00a0Cisco Blogs\u00a0<\/p>","protected":false},"excerpt":{"rendered":" <\/p>\n The hype triggered by the emergence of generative artificial intelligence (AI) feels a lot like the early days of cloud, bringing the topic\u2014and the need for a strategy\u2014to the front of the IT leader ag\u2026 Read more on Cisco Blogs<\/a><\/p>\n \u200b<\/p>\n The hype triggered by the emergence of generative artificial intelligence (AI) feels a lot like the early days of cloud, bringing the topic\u2014and the need for a strategy\u2014to the front of the IT leader agenda.<\/p>\n But while AI is poised to change every aspect of our lives, the complexity of AI infrastructure and operations is holding things back. At Cisco, we believe AI can be a lot easier when we find ways to avoid creating islands of operations and bring these workloads into the mainstream.<\/p>\n AI workloads place new demands on networks, storage, and computing. Networks need to handle masses of data in motion to fuel model training and tuning. Storage needs to scale effortlessly and be closely coupled with compute. Plus, computing needs to be accelerated in an efficient way because AI is seeping into every application.<\/p>\n Consider video conferencing. In addition to the familiar CPU-powered elements like chat, screen sharing, and recording, we now see GPU-accelerated components like AI inference for real-time transcription and generative AI for meeting minutes and actions. It\u2019s now a mixed workload. More broadly, the demands of data ingest and preparation, model training, tuning, and inference all require different intensities of GPU acceleration.<\/p>\n IT teams are being asked to stand up and harden new infrastructure for AI, but they do not need new islands of operations and infrastructure or the complexity that comes with them. Customers with long-standing operating models built on solutions like FlexPod and FlashStack can bring AI workloads into that same domain of simplicity, scalability, security, and control.<\/p>\n The constituent technologies in these solutions are ideal for the task:<\/p>\n UCS X-Series Modular System with X-Fabric technology allows for flexible CPU\/GPU ratios and cloud-based management for computing distributed anywhere across core and edge. We\u2019re working hard with our ecosystem partners to pave a path for customers to mainstream AI. I\u2019m pleased to announce an expanded road map of Cisco Validated Solutions on proven industry platforms, along with new automation playbooks for common AI models.<\/p>\n These solutions span virtualized and containerized environments, multiple converged and hyperconverged infrastructure options, and important platforms like NVIDIA AI Enterprise (NVAIE).<\/p>\n <\/a>Integrated, validated solutions on proven platforms.<\/strong><\/em><\/p>\n These solution frameworks rely on our three-part approach:<\/p>\n Mainstreaming<\/strong> AI infrastructure to reduce complexity across core, cloud, and edge. \u201cBuilding on a decade of collaboration, Cisco and Red Hat are working together to help organizations realize the value of AI through improved operational efficiencies, increased\u00a0 productivity and faster time to market. Cisco\u2019s AI-focused Cisco Validated Design can help simplify, accelerate and scale AI deployments using Red Hat OpenShift AI to provide data scientists with the ability to quickly develop, test and deploy models across the hybrid cloud.\u201d <\/em>\u2014Steven Huels, Senior Director and General Manager, Artificial Intelligence Business, Red Hat.<\/em><\/strong><\/p>\n AI\u2019s infusion into every industry and application will continue to accelerate, even as the component technologies each make their way through the hype cycle to adoption. Increased data collection and computing power, advancements in AI frameworks and tooling, and the generative AI revolution\u2014are all fueling change. Let us help you build on trusted architectures and take these workloads mainstream for maximum effect.<\/p>\nAI is driving big changes in data center technology<\/strong><\/h2>\n
Using proven architectures for operational simplicity<\/strong><\/h2>\n
\nThe Cisco AI\/ML enterprise networking blueprint<\/a> shows how Cisco Nexus delivers the high performance, throughput, and lossless fabrics needed for AI\/ML workloads; we believe Ethernet makes the ideal technology for AI\/ML networking due to its inherent cost-efficiency, scalability, and programmability.
\nHigh-performance storage systems from our partners at NetApp and Pure complete these solutions with the scalability and efficiency that large, growing data sets demand.<\/p>\nIntroducing new validated designs and automation playbooks for common AI models and platforms<\/strong><\/h2>\n
\nOperationalizing<\/strong> and automating AI deployments and life cycle with validated designs and automation playbooks.
\nFuture-proofing<\/strong> for emerging component technologies and securing AI infrastructure with proactive, automated resiliency, and in-depth security.<\/p>\nThe momentum is real; let\u2019s build for the future<\/strong><\/h2>\n
Join our December 5 webinar:<\/strong><\/h3>\n
It\u2019s Time to Simplify AI Infrastructure and Operations<\/a><\/strong><\/h2>\n
AI is driving big changes in data center technology<\/strong><\/h2>\n
Using proven architectures for operational simplicity<\/strong><\/h2>\n
\nThe Cisco AI\/ML enterprise networking blueprint<\/a> shows how Cisco Nexus delivers the high performance, throughput, and lossless fabrics needed for AI\/ML workloads; we believe Ethernet makes the ideal technology for AI\/ML networking due to its inherent cost-efficiency, scalability, and programmability.
\nHigh-performance storage systems from our partners at NetApp and Pure complete these solutions with the scalability and efficiency that large, growing data sets demand.<\/p>\nIntroducing new validated designs and automation playbooks for common AI models and platforms<\/strong><\/h2>\n
\nOperationalizing<\/strong> and automating AI deployments and life cycle with validated designs and automation playbooks.
\nFuture-proofing<\/strong> for emerging component technologies and securing AI infrastructure with proactive, automated resiliency, and in-depth security.<\/p>\nThe momentum is real; let\u2019s build for the future<\/strong><\/h2>\n
Join our December 5 webinar:<\/strong><\/h3>\n
It\u2019s Time to Simplify AI Infrastructure and Operations<\/a><\/strong><\/h2>\n