A practitioner-verified, text-first engineering curriculum (12–24 Months) grounded entirely in official vendor documentation, university courses, foundational textbooks, and working code. Zero video tutorial fluff.
🌐 Live Interactive Checklist & Curriculum: https://wyrcan-io.github.io/roadmap/
Most online roadmaps fall into one of two traps:
- Tutorial Hell: A list of 40-hour video playlists where you copy code without building real mental models.
- Disconnected Toy Repos: Building 20 separate "Hello World" projects that don't reflect production engineering.
This roadmap is built on 4 non-negotiable principles:
- 70% Building: Use books and official docs to unblock real implementations. Building is the actual curriculum.
- One Evolving System: Build and evolve a single production-grade AI platform across your 12 milestones.
- Break Things: Deliberately induce failures, trace errors through telemetry, fix them, and write postmortems.
- Design First: Reason methodically about traffic, consistency, latency, cost, and failure modes before writing code.
Mastering 4 canonical textbooks (ISLP, UDL, DDIA, OSTEP), 7 major platform stacks, and 110 checkpoints is cognitively intense. Rather than pretending this is a quick 6-month bootcamp, we provide two explicit, realistic tracks:
| Dimension | 🛠️ Working Practitioner Track (Recommended) | ⚡ Accelerated Sprint Track |
|---|---|---|
| Total Duration | 24 Months (2 Years) | 12 Months (1 Year) |
| Weekly Commitment | 8–10 hours / week (1 hr/day + weekend build block) | 20–25+ hours / week (full-time daily commitment) |
| Pace per Milestone | ~8 weeks / 2 months per stage | ~4 weeks / 1 month per stage |
| Target Audience | Working engineers, full-time professionals, students | Dedicated career sabbaticals / full-time bootcampers |
| Pedagogical Balance | 4 weeks reading & labs + 4 weeks building, failure testing & runbooks | 2 weeks reading/labs + 2 weeks architecture provisioning & exit test |
| System Design (DDIA) | 1 chapter every 6–8 weeks (deep retention & synthesis) | 1 chapter every 3–4 weeks (high cognitive load) |
| Stage | Track | Core Focus | Primary Deliverable | Recommended Pace |
|---|---|---|---|---|
| 00 | Foundation Reset | Python Model, Asyncio, OSTEP OS, MDN HTTP, PostgreSQL | Trace web request from DNS to DB query plan | 6–8 Weeks (Fast: 3–4w) |
| 01 | Platform | Linux Internals, CLI Fluency, Networking, systemd | Deploy hardened FastAPI service on Linux VM behind Nginx | 8 Weeks (Fast: 4w) |
| 02 | Platform | Docker Deep Dive, Multi-Stage Builds, OCI, Compose | Production multi-tier compose stack with networking isolation | 8 Weeks (Fast: 4w) |
| 03 | Platform | AWS Architecture, VPC Subnets, IAM, RDS, S3, ALB | Provision AWS multi-tier architecture + System Design Doc | 8 Weeks (Fast: 4w) |
| 04 | Platform | GitHub Actions, CI/CD, 12-Factor App, OWASP Security | Automated CI/CD pipeline with Trivy scans & zero-downtime rollback | 8 Weeks (Fast: 4w) |
| 05 | Platform | Terraform IaC, Remote State S3/DynamoDB, Reusable Modules | Spin up and tear down staging cloud environments via Terraform | 8 Weeks (Fast: 4w) |
| 06 | AI / ML | ML Fundamentals, ISLP Python Labs, scikit-learn MOOC | Classical tabular ML pipeline service exposed via FastAPI | 8 Weeks (Fast: 4w) |
| 07 | AI / ML | PyTorch, Autograd, Custom Training Loops, UDL Book | Train PyTorch model from scratch + GPU inference API | 8 Weeks (Fast: 4w) |
| 08 | MLOps | Rules of ML, MLflow Tracking, Model Registry, Lineage | Reproducible ML training pipeline with versioned artifacts | 8 Weeks (Fast: 4w) |
| 09 | MLOps | Continuous Training (CT), Automated Quality Gates | Automated model promotion with canary deployment routing | 8 Weeks (Fast: 4w) |
| 10 | Platform | Kubernetes (EKS / K3s), Pods, Ingress, Helm Charts | Package and orchestrate application in Helm across Kubernetes | 8 Weeks (Fast: 4w) |
| 11 | Production | OpenTelemetry, Prometheus, Grafana, Drift & SLOs | Telemetry dashboards + induced failure incident postmortem | 8 Weeks (Fast: 4w) |
| 12 | Capstone | Architecture Hardening, C4 Diagrams, Well-Architected Review | Production portfolio platform package & security audit | 8 Weeks (Fast: 4w) |
| ☵ | System Design | DDIA 2nd Edition, Microsoft Cloud Patterns, Builders' Library | Parallel continuous architecture loop (Stages 01–12) | Continuous |
If the entire catalog feels vast, these 13 items form your core intellectual spine:
- Foundation: MIT Missing Semester + OSTEP + Pro Git + MDN HTTP
- Linux & Networking: The Linux Command Line + Beej's Guide to Network Concepts
- Docker: University of Helsinki DevOps with Docker + Docker Build Best Practices
- AWS: AWS Skill Builder + Well-Architected Framework + Amazon Builders' Library
- DevOps: GitHub Actions + Google SRE Books
- Terraform: HashiCorp AWS Tutorials + Modular IaC
- ML Fundamentals: An Introduction to Statistical Learning (ISLP) + Google MLCC + scikit-learn MOOC
- Deep Learning: PyTorch Basics + Understanding Deep Learning (Prince / MIT Press)
- ML Engineering: Google's Rules of ML + Hidden Technical Debt in ML (NeurIPS) + MLflow
- MLOps: Google Cloud MLOps Guide + DataTalksClub MLOps Zoomcamp
- Kubernetes: Kubernetes Official Tutorials + Helsinki K8s MOOC + Amazon EKS Workshop
- Observability: OpenTelemetry Primer + Prometheus + Evidently AI
- System Design (Continuous): Designing Data-Intensive Applications (DDIA 2nd Edition) + Microsoft Cloud Design Patterns + Amazon Builders' Library + C4 Model
The live interactive site includes:
- Google Docs-style live Outline panel with scrollspy tracking.
- 110 Granular Checkpoints saved locally in browser storage.
- Warm Dark Mode (Espresso) & Light Mode (Warm Ivory) toggle.
- Export Progress to Markdown for Notion / Obsidian / personal dev journals.
- Live Search Filter across all 59 primary sources and documentation chapters.
Maintained with care by Wyrcan as a 100% free and open public resource for engineers worldwide.