GuideAdvanced
Deployment & Cloud Infrastructure Guide
Master deployment strategies for AI systems: understand when to use Local, Serverless, Managed or Self-hosted; learn AWS (S3, Lambda, SageMaker basics) with LocalStack at zero cost; evaluate modern alternatives (Render, Railway, Fly.io); and build a complete decision matrix to choose the right deployment strategy. From Docker containers to production-deployed AI systems.
- 64
- lessons
- 8
- modules
- English · Spanish
- available in
- Yes
- certificate
- Free
- access
Outcomes
What you'll be able to do
- Build a decision matrix to choose between Local, Serverless, Managed, and Self-hosted deployment strategies
- Deploy multi-container AI apps locally with Docker Compose and production-like configuration
- Implement serverless AI endpoints with AWS Lambda, including cold start handling and timeout configuration
- Develop and test cloud pipelines locally with LocalStack (S3, Lambda) at zero cost
- Integrate AWS services (S3, Lambda, SageMaker basics) for AI applications
- Apply cloud migration patterns: same code running on LocalStack and AWS with environment abstraction
- Evaluate and deploy to alternative platforms: Render, Railway, Fly.io
- Deploy a complete AI system to production with validated decision matrix and operational runbook
Before you start
What you need to bring
It's for you if...
- AI Engineers with containerized apps and CI/CD pipelines ready to choose deployment strategy and platform
- Backend developers expanding to cloud who need to evaluate AWS vs modern alternatives (Render, Railway, Fly.io)
- Tech leads preparing cloud infrastructure for AI systems who want decision frameworks and LocalStack for zero-cost learning
- Developers who want to learn AWS without spending money (LocalStack as gateway)
- Teams transitioning from "works on my machine" to production-deployed AI systems
Requirements and materials
- Docker Essentials (Guide #15): images, containers, Docker Compose
- CI/CD for AI Systems (Guide #16): GitHub Actions, deployment pipelines
- Python intermediate: FastAPI, Pydantic, async/await
- Experience with at least one functional AI app (RAG, agent, chatbot)
- Basic knowledge of REST APIs and HTTP
- Git and GitHub for version control
Content
The syllabus, module by module
Open any of them to see its lessons.
- 1. Introduction: Alternative Platforms (Render, Railway, Fly.io)
- 2. Render: Deployment for AI Apps
- 3. Railway: Deployment for AI Apps
- 4. Fly.io: Deployment for AI Apps
- 5. Platform Comparison: Render vs Railway vs Fly.io
- 6. CI/CD Integration with Alternative Platforms
- 7. Platform Decision Matrix: The Complete Framework
- 8. Project: Deploy to Platform of Choice
- 1. Introduction: Integrative Project — Deployed AI System
- 2. Architecture Integration — Connecting the Pieces
- 3. Deployment Automation — From Git Push to Production
- 4. Post-Deploy Validation — Verify That It Really Works
- 5. Operational Runbook — What to Do When Something Fails
- 6. Final Decision Matrix — Document Your Decision
- 7. Performance Baseline — Establishing Reference Metrics
- 8. Final Project: Deployed Production AI System
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
No limit. It's a free guide: come in whenever you like, as often as you like.
No. Modules run from easier to harder, but you can jump to the one you need. Progress is saved per lesson.
Whatever is needed is listed under “What you need to bring”, above. If nothing is listed there, you can start from zero.
In the Club's WhatsApp group, and every two weeks there's a live with an instructor where questions get worked through.
Yes. It's issued automatically once you finish every lesson, with a verifiable code you can share on LinkedIn.
No. This guide is self-paced with no dates. The bootcamp is live, by cohort, with work someone reviews.
Start whenever you like
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