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Beyond the Demo: Architecting AI Systems for Production
Role Play
New
Rating: 4.9 out of 5(27 ratings)
198 students

Beyond the Demo: Architecting AI Systems for Production

Build AI systems that scale in production — practical frameworks for all levels, no math, no hype.
Role Play
New
Created byEl Guenbouri Hamza
Last updated 7/2026
English
English [Auto],

What you'll learn

  • Design software architectures that integrate AI components like LLMs, embeddings, and vector search into production systems
  • Apply four core AI architecture patterns: API-First, RAG, AI Agents, and Human-in-the-Loop
  • Make informed build vs buy vs prompt decisions using a structured decision framework
  • Avoid the most common and costly AI architecture anti-patterns that teams make in production

Course content

5 sections • 19 lectures • 2h 23m total length

  • Introduction2:27
  • How AI Changes Software Architecture4:42
  • Designing for Uncertainty3:20

    Design an architecture that treats llm calls as contained, expensive components, using a production prompt builder, cost guardrails, an evaluation layer, and a continuous feedback loop for verifiable outputs.

  • Your Role as an Architect8:15
  • Build vs Buy vs Prompt — Decision Framework9:45
  • AI Capabilities & Limitations for Architects7:46
  • Section 1 Quiz: The AI Shift

Requirements

  • Basic understanding of software architecture concepts (APIs, microservices, databases)
  • No machine learning or AI experience required
  • Familiarity with at least one backend development

Description

Adding AI to a system is easy. Architecting one that holds up in production is not.

Anyone can call an API and get a working demo. But when real users show up, most AI systems start to crack — answers drift, costs spike, data leaks, and nothing in the logs tells you why. The gap between a demo that impresses and a system that survives is the most valuable skill in software today. This course is built to give you that skill.

AI changes a fundamental rule. Traditional software is predictable — same input, same output, every time. AI systems make decisions, interpret, and generate, and the same input won't always give the same answer. That single shift breaks much of what you know about designing reliable systems, and it puts a new set of decisions on your plate — decisions no one trained you to make.

This course teaches you how to make them well. Every concept is anchored to a clear mental model and a real production example, so it actually sticks. No math-heavy theory. No hype. You'll finish with practical frameworks you can bring straight into your next architecture review.

You already know how to build software that works. This course teaches you how to build software that thinks.

Who this course is for:

  • Software architects designing systems that integrate AI capabilities
  • Senior developers and tech leads evaluating AI for their products
  • Engineering managers making build vs buy decisions for AI features
  • DevOps and security engineers preparing infrastructure for AI workloads

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