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FlexPod AI simplifies your AI/ML Challenges Now

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Bruno Messina
Bruno Messina

Artificial intelligence and machine learning (AI/ML) are revolutionizing industries, driving innovation, and unlocking new possibilities. To harness the full potential of AI/ML and to fine-tune workloads, your organization needs a robust infrastructure that can handle the demanding operational, GPU, computational, and storage requirements. The FlexPod® converged infrastructure, a joint Cisco and NetApp® platform, offers the powerful solution that you need. FlexPod simplifies AI/ML infrastructure provisioning, accelerates deployments, lowers risk, increases security, unifies data, and facilitates scalability.

This blog post explores the innovative combination of FlexPod and NetApp ONTAP® technology, and it highlights the compelling reasons that your organization should choose FlexPod for your AI/ML initiatives.

Flexpod A Cisco and NetApp Solution

Bring the FlexPod platform to AI/ML workloads

FlexPod AI, powered by Cisco and NetApp technology, simplifies AI infrastructure by tightly coupling compute, network, storage, and GPU resources with extensive end-to-end testing and one-call support. Verification and integration reduce complexity and eliminate silos, providing your organization with a unified and efficient infrastructure foundation for your AI/ML workloads. 

Your organization must have a robust infrastructure that can handle the data, operational, GPU, computational, and storage requirements of AI/ML. FlexPod delivers these attributes and much more. With FlexPod, your organization can harness the full potential of AI/ML. FlexPod is also an optimal infrastructure for training of internal models, fine-tuning of open-source models, and inferencing. In addition, FlexPod AI also supports both container-based and VM-based IT shops, giving you more flexibility.

Streamline and speed AI/ML deployments

With the proven FlexPod platform and its reference architectures (Cisco Validated Designs [CVDs] and NetApp Verified Architectures [NVAs]), you can easily operationalize and accelerate AI/ML deployments. These reference architectures are fully tested—from A to Z—blueprints on how selected components scale, wire, deploy, and provision AI/ML workloads. You can reduce risk from your infrastructure deployments; simplify your operations; and increase your system’s long-term scalability, resilience, and security.

For more information about FlexPod business simplification and operational benefits, download the Forrester infographic or white paper.

total economic impact of Cisco and Flexpod NetApp title page

FlexPod AI uses the NetApp ONTAP unified data storage platform, which has scale-out capabilities. Your organization can efficiently manage and expand your data storage as your AI/ML workloads grow. This scalability enables seamless performance and flexibility for your organization’s evolving AI initiatives. FlexPod AI also makes full use of NetApp expertise in data management, industry-leading data protection, simplified data movement, and data governance across on-premises and cloud environments.

You also gain access to the NetApp DataOps Toolkit, which makes it simple for your developers, data scientists, DevOps engineers, and data engineers to perform data management tasks within a bare-metal or Kubernetes cluster. NetApp DataOps Toolkit, your teams can also:

  • Launch and provision Jupyter Notebooks to easily work with familiar frameworks. This familiarity streamlines data management, accelerates AI workflows, and simplifies inferencing.
  • Clone large data volumes instantly and run inference tasks effortlessly, enabling your data science teams to spend far less time on administration and more time on innovation.
  • Launch an NVIDIA Triton Inference Server so that models can be validated or moved into production.

FlexPod AI also provides seamless NVIDIA AI Enterprise integration of general-purpose AI workloads into existing VMware and Red Hat OpenShift environments. In other words, both Kubernetes containers and VMs benefit from FlexPod AI and the NetApp DataOps Toolkit.  

Flexpod AI simplifies logo

Safeguard data with FlexPod cybersecure architecture

FlexPod AI protects your AI/ML workloads and data with a comprehensive approach to safeguarding systems, management processes, data, and applications. Trust is established and enforced through device hardening, microsegmentation, least-privilege access, and an end-to-end secure value chain. FlexPod AI goes above and beyond by encrypting data in transit and at rest, and its Zero Trust architecture, featuring multi-admin verification and multifactor authentication, thwarts rogue actors. With FlexPod, your infrastructure is fortified against threats, and your digital assets are kept secure.

Begin a successful AI journey with FlexPod

Choosing the right infrastructure is a crucial step for organizations as they begin to implement their AI initiatives. FlexPod AI offers a powerful solution that simplifies your AI infrastructure, speeds deployments, and maintains scalability and security. With tightly coupled resources, validated solutions, automation playbooks, and advanced data management capabilities, FlexPod AI empowers your organization to unlock the full potential of AI/ML, to drive innovation, and to achieve transformative outcomes. With FlexPod AI, your organization can embark on a successful AI journey with confidence.

Start your FlexPod AI Journey Today

Take the first step toward making the most of AI/ML to drive innovation in your organization. To learn more about how FlexPod and its reference architecture portfolio can enhance your AI/ML workloads, visit:

Bruno Messina

Bruno Messina joined NetApp in 2018 and works in product marketing for FlexPod. His previous experience includes a career in product marketing of UCS servers for Cisco Systems and Solaris server marketing and competitive analysis at both Oracle and Sun Microsystems, where he joined in 2000. Bruno spent ten years in various roles of competitive analysis and product management at Sun Microsystems, leading analysis in both the workgroup and enterprise servers. Prior to Sun Microsystems, Bruno spent time finishing his MBA education and worked for two years at Cadence working on product marketing for both board-level and board timing tools. Bruno holds both a BSEE and MBA from Rensselaer Polytechnic Institute in Troy, N.Y.

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