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System Design & Scaling Guide

Master architectural thinking for AI systems: scaling strategies, reliability patterns, integration patterns (webhooks, event-driven, MCP), real-world channels (Slack/Discord), and performance vs cost trade-offs. This synthesis guide connects everything you've learned—Docker, CI/CD, deployment, monitoring, cost optimization, security—and teaches you to DESIGN production-ready systems. Culminates with the complete architecture design for the Capstone: AI-Powered Knowledge Assistant with Agentic RAG and Slack/Discord integration.

64
lessons
8
modules
English · Spanish
available in
Free
access
NIEVA

Outcomes

What you'll be able to do

  • Apply architectural thinking specific to AI systems (constraints, trade-offs, design documents)
  • Compare and choose between monolith, microservices, and event-driven architectures for AI
  • Design scaling strategies: horizontal vs vertical, auto-scaling, stateless design
  • Implement integration patterns: webhooks, event-driven, MCP (Model Context Protocol)
  • Design reliability at scale: load balancing, queue-based processing, rate limiting
  • Design Slack and Discord bots as production channels (OAuth, webhooks, best practices)
  • Apply decision frameworks for performance vs cost, managed vs self-hosted, cache vs compute
  • Design the complete architecture for the Capstone: AI-Powered Knowledge Assistant

Before you start

What you need to bring

It's for you if...

  • AI Engineers who completed guides #1-#20 and need to learn system design thinking
  • Developers preparing for the Capstone who want the architecture blueprint first
  • Tech leads and architects designing AI systems for their teams
  • Engineers who want to make informed decisions about scaling, integrations, and trade-offs
  • Developers seeking portfolio-worthy architecture design documents

Requirements and materials

  • Completion of AI Engineering Path guides #1-#20 (Docker, CI/CD, Deployment, Monitoring, Cost Optimization, Security)
  • Experience deploying and monitoring AI applications
  • Understanding of RAG, agents, and production patterns
  • Familiarity with REST APIs, webhooks, and async patterns
  • Basic experience with architecture diagrams (or willingness to learn)

Content

The syllabus, module by module

Open any of them to see its lessons.

Where it fits

This guide is part of something bigger

It's studied inside these programs, with support and dates.

Common questions

What people usually ask

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