Design reliable AI infrastructure with secure MLOps, end-to-end observability, and cost control.
Provided by Kevin P., Solutions Architect - Infrastructure and Operations Transformation — Senior Network SME Collaboration, Cybersecurity, DEVOPS
Build an operational foundation for AI workloads that is scalable, observable, secure, and financially controlled.
Scope • Assess model delivery, compute, data flows, pipelines, monitoring, and access • Design MLOps lifecycle controls for deployment, rollback, drift, and governance • Define observability for performance, availability, quality, security, and cost Deliverables • Current-state AI infrastructure assessment • Target MLOps and observability architecture • Monitoring, SLO, alerting, and dashboard specification • Security and access-control recommendations • Phased implementation and cost-optimization roadmap
AI infrastructure support for cloud and hybrid environments. Includes observability, monitoring, model-serving readiness, GPU and platform troubleshooting, scaling guidance, runbooks, cost visibility, and practical remediation for production AI systems that are slow, unstable, or hard to operate.
Final scope is confirmed after initial assessment.
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