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- Cloud
Operating Production AI Workloads in the Cloud
1 Course
0.5 Hours
Skill IQ
Build the operational discipline to run production AI workloads as economically defensible systems — attributing metered spend to delivered value, gating changes on evaluation evidence, and containing failure in workloads that can be technically healthy and semantically wrong at the same time. Choose this path when you already operate production cloud workloads and need to move an organization from AI experimentation into repeatable operation you can defend with evidence.
**This learning path is actively in production. More content will be added to this path as it gets published and becomes available in the library. To view the full list of content to be included in this Path, click the "About this path" tab below.**
Content in this path
Operating Production AI Workloads in the Cloud
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What You'll Learn
- Work out what your AI workloads actually cost per unit of value delivered, and decide when paying up front for capacity beats paying per request
- Decide whether a model, prompt, or retrieval change is good enough to ship — and handle a model being retired as planned work instead of a fire drill
- Tune limits and permissions to a workload's normal so you hear about trouble early instead of reading about it on the invoice
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- 1. Cloud Cost Attribution and Capacity Commitment for AI Workloads \
- 2. Evaluation Gates and Model Lifecycle for Production AI \
- 3. Calibrating Operational Constraints for AI Workloads at Cloud Scale \
- 4. Diagnose a Cost-per-Workflow Regression on AWS \
- 5. Demo: Gate a Prompt Change on Evaluation Evidence in Azure
Prerequisites
- You should already operate production cloud workloads with working fluency in cloud primitives, production architecture, security and IAM, monitoring, and deployment workflows, plus conceptual familiarity with inference, RAG, agents, and model APIs. Traditional cloud cost fluency — cost drivers, tagging, and cloud financial governance — is assumed rather than taught. This curriculum does not cover cloud fundamentals, AI architecture design, or introductory AI terminology.
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