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Achieve public cloud-like service consumption in your on-prem AI data center with Apstra and Terraform. Apstra and the Terraform Provider for Apstra fit traditionally complex network services, like EVPN, neatly into a predefined application automation. This session shows how network teams can self-serve network services in a familiar way, providing seamless deployments across any infrastructure for new AI/ML workloads.
Nick Davey from Juniper Networks discussed how the increasing complexity and scale of modern data centers, particularly for AI and ML workloads, necessitate advanced automation solutions. He introduced Juniper Validated Designs (JVDs) and highlighted the AI JVD, which includes not just architectural diagrams and configurations but also the required automation to bring these designs to life using Apstra. The AI JVD allows for flexible, scalable, and automated deployment of AI data centers, making it possible to adapt to specific network requirements and efficiently manage the intricate configurations needed for AI workloads.
Central to this automation is Apstra, which provides a cloud-like API to manage physical data center resources. Apstra’s design cycle allows network engineers to move from traditional whiteboard planning to orchestrated, automated design and deployment. This process involves designing the network in Apstra, assigning physical resources, and deploying the network using Zero Touch Provisioning (ZTP). Apstra also supports continuous monitoring and optimization, ensuring the network remains in the desired state, which is crucial for handling the demanding and complex nature of AI workloads.
The integration with Terraform further enhances automation capabilities, allowing for bulk operations and dynamic infrastructure management. Terraform’s declarative approach complements Apstra’s intent-based networking, enabling users to manage their data centers as code. This integration facilitates seamless deployment and management of AI data centers, ensuring that network changes and configurations can be version-controlled, tested, and automated. Additionally, the use of ServiceNow as a front-end interface allows non-networking personnel, such as data scientists, to request and provision infrastructure without needing to understand the underlying complexities, thus democratizing access to AI resources and streamlining operations.
Personnel: Nick Davey
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