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Azure AI Solution Architecture Patterns
Learn to architect AI solutions on Azure the way a cloud engineer works any hard problem: reason a workload down to the engineering it actually requires, resolve it into Azure services, and defend the result under real cost and security constraints. This path builds durable judgment across inference, model customization, retrieval, agents, and production operations. Choose it if you know cloud fundamentals and need to move into AI architecture without becoming a machine-learning specialist.
Content in this path
Framing the Problem
Build the reasoning method the rest of the path depends on: how to translate an AI or ML workload into the compute, data, identity, and cost decisions it implies, and how to derive the minimal architecture it actually requires. Start here — every later section applies this lens to a specific pattern.
Architecting the Patterns
Work the core AI solution patterns — inference and model customization, retrieval-augmented generation, and agentic systems — from workload to a defensible Azure architecture. Each course reasons through the key trade-offs and design questions, then resolves them into Azure services you can stand behind in a design review.
Operating the Solution
Keep AI workloads healthy, bounded, and accountable once they're live, integrating evaluation, guardrails, monitoring, and audit evidence into Azure's operational stack. Then put it into practice by building and validating a governed agent hands-on.
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What You'll Learn
- Reason any AI workload down to the minimal engineering it requires, translating AI and ML terms into concrete compute, data, identity, and cost decisions
- Architect and defend inference, customization, retrieval, and agentic solutions on Azure, resolving each pattern into the right services under real constraints
- Operate AI workloads in production on Azure, integrating evaluation, guardrails, monitoring, and audit evidence to keep them healthy, bounded, and accountable
- You should already work confidently as a cloud engineer or architect — comfortable with compute, storage, identity and access, networking, and cost trade-offs on Azure or a comparable cloud. Basic familiarity with common AI terms such as models, prompts, retrieval, and agents will help, but the path opens by translating that vocabulary into engineering terms, so deep AI experience isn't required. This curriculum does not teach machine-learning theory, model training, or data science; it focuses on the architecture decisions a cloud professional makes when AI is the workload.
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