The week-by-week topics, labs, and reference resources I use to run my live bootcamp. Learn More
Prerequisites: basic Linux CLI, comfort with Bash or Python, an AWS Free Tier account.
| Week | Module | Tools | What you build | AI accelerates this |
|---|---|---|---|---|
| 1–2 | AWS Cloud Fundamentals | AWS, Terraform, IAM, EKS, Lambda | Custom VPC + IAM setup | Claude generates the templates, you decide the architecture |
| 2–3 | Docker & Kubernetes | Docker, Helm, ArgoCD | Containerised app on EKS cluster | AI writes the Dockerfile, you own the image hardening |
| 3–4 | Terraform + CI/CD | Terraform, GitHub Actions | Full IaC pipeline with security scanning | AI scaffolds the modules, you own state management |
| 4 | SRE & Observability | Grafana, Prometheus | Live incident simulation with dashboards | AI generates dashboard JSON, you calibrate the thresholds |
| 5 | AI / RAG / AgentOps | pgvector, LLM APIs, Ollama | Text-to-SQL RAG agent deployed to K8s | This IS the AI project — you own the guardrails |
Get these out of the way so we don't burn live time on setup.
- AWS Free Tier account, IAM admin user with access keys, AWS CLI installed
- Docker Desktop installed and
docker run hello-worldworks - Terraform CLI installed (
terraform -v) kubectlandhelminstalled- VS Code (or your editor) + Git configured
Light pre-reading if you have time:
- Understanding of OSI Model by Cloudflare
- IP Address/CIDR Tool
- LFS101 — Intro to Linux Chapters 1–5
- Introduction to Git & GitHub
Modules
- AWS Cloud Practitioner Essentials
- Virtual Private Cloud - VPC
- AWS EC2
- AWS IAM - Lab builder
- AWS RDS - PostgreSQL
Modules
- Enable WSL (Windows Susbsystem for Linux) in Windows
- Docker Desktop Setup - Windows
- Docker Introduction
- Enable Kubernetes in Docker Desktop with WSL
- Kubernetes overview
Modules
Modules
Modules
- How I use LLMs by Andrej Karpathy
- AI Agent Skills by IBM
- AI Agents MCP
- Retrieval-Augmented Generation - RAG
- Claude Code Introduction by Anthropics
db-agent — text-to-SQL AI agent deployed on Kubernetes
- SQL safety guardrails, full CI/CD pipeline, presented at AAAI-25
- Available as Streamlit, Next.js+FastAPI, and native Databricks App
AWS enterprise data lake — three-lab sequence for building a production-grade data lake on AWS
- Lab 1: S3 raw/curated zones, Glue PySpark ETL, Parquet, Athena
- Lab 2: event-driven ingestion — S3 triggers, SQS, Lambda, Redshift
- Lab 3: Lake Formation governance — row/column/tag-based access controls, CDC via DMS
- Entire environment provisioned with Terraform, per-student sandboxed IAM
| AI commoditized this | Engineers still own this |
|---|---|
| Writing Terraform from scratch | State management, multi-account strategy |
| Dockerfile boilerplate | Image hardening, multi-stage builds |
| K8s manifest YAML | CNI, admission webhooks, cluster security |
| Basic alerting rules | Calibrating thresholds, incident command |
| RAG pipeline scaffolding | Chunk strategy, retrieval quality, guardrails |
AI wrote this table. I decided what goes in each column.
Option A — Self-paced: Work through the modules on your own. Everything links to the relevant open-source tools. No signup required.
Option B — With the cohort: Weekly live session on Mondays 6–8 PM EDT. Slack support throughout the week. Access to the TorontoAI founder and recruiter network (10,000+ members). $299 CAD one-time. becloudready.com
Option C — Corporate: Private team workshops for Databricks and cloud engineering teams. Book a call
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