Reference Architectures
Cloud and data flow diagrams, decision records, and guardrail policies.
AI-Augmented Cloud Computing for Technologists
Design, deploy, and operate AI-ready cloud systems. Ten applied modules, GitHub labs, and portfolio-grade outcomes — no tests, just real work.
- 10 modules
- 20+ labs & templates
Who It's For
Solution architects, DevOps and platform engineers, SREs, and data/ML practitioners who want a practical, end-to-end view of cloud systems with AI workloads.
Prerequisites
- Comfort with Linux and Git
- Basic scripting (bash/python) and YAML
- Access to a major cloud (or emulator) and Docker
10-Module Learning Path
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1 — Cloud Foundations & Models
Deliverable: Build a secure, tagged VPC and baseline policy.
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Deliverable: Provision and report resources via CLI/SDK and IaC.
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3 — Virtualization & Containers
Deliverable: Containerize an app and publish to a registry.
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4 — Kubernetes & Service Deployment
Deliverable: Deploy a microservice stack with Helm, ingress, and network policies.
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5 — Scaling, Resilience & Chaos
Deliverable: Implement autoscaling and run a chaos experiment with health probes.
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6 — Serverless & Edge
Deliverable: Build and test a serverless workflow; deploy an edge function.
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Deliverable: IaC-driven ETL pipeline across object store and database.
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Deliverable: Launch an end-to-end ML workflow with managed services.
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Deliverable: Build dashboards and cost reports; define alerting SLOs.
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Deliverable: Live demo + architecture brief integrating Modules 1 — 9.
What You — ll Build
Automated Environments
IaC templates, CI/CD pipelines, and Helm-based service deployments.
AI-Enabled Workloads
Serverless functions, streaming jobs, and ML inference endpoints.
Generative AI Integration
Use ChatGPT, Claude, or Gemini to scaffold code, generate diagrams, draft runbooks, and troubleshoot. Always validate outputs, document prompts, and include them with your artifacts.
- Learning — have AI explain concepts and compare trade-offs
- Creating — generate starter code, IaC, and diagrams
- Presenting — outline demos and architecture briefs
Materials & Support
- On-demand video demos (2 — 3 per module)
- GitHub repo with lab templates
- Architecture, security & cost checklists
- Community Slack channel
- Monthly live office hours
FAQ
How long does it take?
Most learners finish in 6 — 10 weeks at 3 — 5 hours per week. It's fully self-paced or can run as a team cohort.
Which cloud providers are used?
Concepts are vendor-neutral. Labs reference mainstream services with notes for common equivalents.
What will I need installed?
Docker, Git, kubectl, and access to a cloud environment (or a local emulator), plus a preferred AI assistant.
Is there a certificate?
Yes, you — ll earn a digital credential for completing required modules and the Cloud Challenge.