Insight AI部署引擎|戴尔AI工厂

Insight AIDeployment Engine Executive summary Key benefits What it is.The AI Deployment Engine (ADE) is Insight’s proprietary automationframework that accelerates and standardizes AI platform delivery acrossdata center and cloud. By packaging validated architectures and automating Faster time to valuethrough automation and Operational confidencevia built-in security Why it matters.AI outcomes stall when teams wrestle with infrastructurecomplexity, fragmented toolchains, and skills gaps. ADE removes friction bypackaging proven reference architectures, automating configuration, and Scalablearchitecture alignedto validated designs What you get.A working AI platform aligned to validated designs(Dell + NVIDIA), with baseline security, observability, and performanceguardrails — delivered through Insight’s PLAN-BUILD-MANAGE methodology 1. The problems: Speed, risk, and scale Enterprise AI programs typically slow down at the transition from slides tosystems. Integration across compute, storage, networking, Kubernetes,security, MLOps, and AIOps is non-trivial; inconsistent patterns andmanual steps inflate cost, extend timelines, and create operational risk. From a business perspective, the key levers aretime to value, predictability,andcompliance. By enforcing an opinionated reference architectureand compatibility matrix, ADE reduces variance across deployments Insight AI Deployment Engine 2. What’s under the hood(curated, not custom) What ADE is (in business terms) ADE is Insight’ssoftware accelerator/deploymentenginethat installs, configures, and validates the AIplatform. During delivery, ADE introduces deploymentartifacts (tools, configurations, files, settings) required tomaintain integrity post-deployment — our IP that ensures Rather than inventing bespoke stacks, ADEaligns tovalidated designsso you benefit from the latest •Generative AI training & platform patterns(Dell/NVIDIA): full-stack considerations for trainingand production posture. Executives can think of ADE as astandard operatingmodel for platform build-out: a repeatable, automatedpath that increases quality while reducing elapsed time For high-performance use cases, ADE validatesreadiness forAI fabric networks(e.g., RDMA/InfiniBand,Spectrum-X) that enable non-blocking data 3. 5.Security, governance, andobservability (designed-in) How ADE works(without the jargon) ADE drives a phased rollout, which is sequenced for riskreduction and measurable progress at each step: Security isn’t bolted on at the end; ADE introducespolicy baselines, role-based access control, networksegmentation, and secrets managementas part of the standard bring-up. Observability is included by default,with telemetry, logging, and metrics pipelines enabling 1.Physical readiness(rack/stack, firmware, 2.Core Kubernetes(secure cluster foundation) 3.Dev & deployment tooling(CI/CD, registries,automation)4.Data science tooling(notebooks, curated frameworks)5.Security & connectivity(identity, policy, hybrid links)6.Performance & Observability(SLOs, GPU/IO tuning) This “Tactical Platform— Deployment Engine” approachis the backbone of our internal playbooks and executivereadouts. It ensures fast progress while preserving a trail Insight AI Deployment Engine 6. 8. Delivery model & timeline FAQs ADE is embedded in our PLAN–BUILD–MANAGEdelivery motion: How does ADE fit with our cloud strategy? ADE supports hybrid and multicloud patterns;it standardizes the control plane and pipelines soworkloads can land where compliance and •PLAN (RADIUS™)— Strategy, feasibility, and aprioritized backlog aligned to business outcomes.•BUILD (Implementation)— ADE-driven platforminstallation, configuration, and validationagainst SLOs. What if we don’t have internal MLOps/AIOps maturity? ADE stands up the baseline toolchain and runbooks; ourDEVSHOP model fills the gap while your teams upskill. How do you manage network bottlenecks for training? We validate fabric readiness (RDMA/InfiniBand,Spectrum-X) during early phases to ensure 7. Outcomes & evidence •Faster time to value:Standardized builds andautomation reduce manual effort and shortencritical path dependencies across infrastructure,platform, and security workstreams.•Operational confidence:Validated configurations References (Internal) •InsightAILandscape2025v1andInsightAILandscape_2025— ADE positioning, capabilities, offers.[1][2]•AI Platform V1 Executive ReadoutandAI Infrastructure Solutions V1 Executive Readout— phase model and partner alignment.[10][4]•Service Overview - AI Enabled Products + Services - AI Platform v0-1— ADE IP/licensing language.[9]•services-for-gen-ai-customer-deck— MLOps/AIOps integration activities.[3]•Nvidia AI Advisor 20250806— AI fabric networking guidance.[12]