AI App Architecture & Deployment Patterns
Learn to architect and deploy production-ready AI applications — from choosing between monoliths and microservices to surviving your first 200-user load test. Master the patterns, deployment strategies, and operational practices that turn working AI services into scalable, secure systems.
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Episode 0: AI App Architecture & Deployment Patterns- Introduction
Meet the FieldAssist team and the challenge ahead: turning disconnected AI backend services into a production application before the Q3 deadline.
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Episode 1: AI Application Architecture Foundations
Map the five architectural layers of AI applications and learn why non-determinism, token costs, and dynamic orchestration change every design decision.
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Episode 2: Architectural Patterns for AI Systems
Compare monolithic, modular monolith, microservices, and event-driven patterns — then use a decision framework to pick the right one for your constraints.
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Episode 3: Deployment Models and Runtime Environments
Choose between serverless, containers, and managed platforms for AI workloads, and build CI/CD pipelines that catch bad prompts before production.
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Episode 4: Scalability, Reliability, and Observability
Survive the 200-user load test with auto-scaling, circuit breakers, distributed tracing, and AI-specific observability that monitors output quality, not just uptime.
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Episode 5: Security, Governance, and Optimization Readiness
Pass the security audit with secrets management, prompt injection defense, and audit trails — then architect for cost optimization without a rewrite.
