AI Engineering, Cloud Architecture, and AI Product Roadmap

This roadmap is a practical 12-month learning system for building production-grade AI systems, cloud architectures, evaluation pipelines, and AI product thinking.

The goal is not to collect random courses. The goal is to build, explain, secure, deploy, evaluate, and improve real systems.

Learning Principles

• Learn concepts deeply enough to explain them without notes.
• Build small implementations before using large frameworks.
• Turn every major topic into a project artifact.
• Measure progress by working systems, not completed videos.
• Keep notes short, reusable, and connected to projects.
• Document trade-offs with architecture decision records.
• Review failures intentionally: broken systems teach faster than perfect tutorials.

How to Use This Folder

• Start with START-HERE.md.
• Use web/ for the responsive web reader that can be deployed to Cloudflare Pages.
• Use PROGRESS.md as the weekly dashboard.
• Use MONTHLY-PLAN.md for week-by-week execution.
• Use daily-routine.md when time is limited.
• Use learning-playbook.md for learning, debugging, documentation, and project habits.
• Use anti-patterns.md to avoid common failure modes.
• Use glossary.md when terms are unclear.
• Use today.md for daily focus.
• Use review-log.md for study and weekly review history.
• Use commands.md for common development commands.

Core Skill Stack

• Machine learning fundamentals
• Deep learning
• Transformers and LLMs
• RAG and vector search
• AI agents and tool calling
• AI evaluation, LLMOps, and MLOps
• Docker and Kubernetes
• Cloud architecture
• Infrastructure as Code
• Security and governance
• AI product discovery and ROI
• Enterprise architecture and system design

Supporting Skills

• Python
• TypeScript
• SQL
• Git and GitHub
• Linux
• REST APIs
• Event-driven architecture
• PostgreSQL
• Redis
• Vector databases
• CI/CD
• Observability
• Networking
• IAM/RBAC
• Cost optimization
• Technical documentation

12-Month Roadmap

Weekly Study System

Use this rhythm every week:

• Monday: theory and notes.
• Tuesday: hands-on implementation.
• Wednesday: architecture reading and diagrams.
• Thursday: implementation and debugging.
• Friday: system design, product, cloud, or security.
• Saturday: project build session.
• Sunday: review, documentation, tracker update, and next-week planning.

Targets:

• Minimum: 10 hours/week
• Ideal: 12-15 hours/week
• Accelerated: 15+ hours/week

Project Tracks

Use these project folders as practical outputs:

• projects/01-business-sales-prediction/README.md
• projects/02-neural-network-classifier/README.md
• projects/03-transformer-llm-lab/README.md
• projects/04-enterprise-rag-platform/README.md
• projects/05-erp-ai-agent/README.md
• projects/06-ai-evaluation-framework/README.md
• projects/07-production-cloud-ai-platform/README.md
• projects/08-ai-transformation-case-study/README.md
• projects/09-final-enterprise-ai-system/README.md

Templates and Trackers

• templates/study-session.md — repeatable study session template.
• templates/project-readme-template.md — standard portfolio README structure.
• templates/adr-template.md — architecture decision record template.
• templates/evaluation-dataset-template.csv — evaluation dataset starter.
• templates/weekly-review.md — weekly review checklist.
• templates/skill-matrix.md — skill self-assessment tracker.
• templates/certification-tracker.md — certification planning tracker.
• templates/system-design-practice.md — weekly system design practice template.

Checklists

• checklists/production-ai-checklist.md
• checklists/rag-checklist.md
• checklists/agent-safety-checklist.md
• checklists/cloud-architecture-checklist.md
• checklists/security-checklist.md
• checklists/project-completion-checklist.md

Data, Diagrams, Labs, and Rubrics

• datasets/README.md — dataset catalog and documentation rules.
• sample-data/ — small CSV and Markdown data for practice.
• diagrams/ — Mermaid diagram templates.
• labs/ — small practice labs before larger projects.
• rubrics/ — objective scoring guides for projects, RAG, agents, architecture, and documentation.
• quality-gates.md — module, project, production-style, and portfolio-ready gates.
• failure-cases.md — failure case bank for testing.
• reading-list.md — curated reference list.
• project-ideas.md — additional project ideas.

AI Learning Prompts

• prompts/explain-this.md
• prompts/project-review.md
• prompts/debugging.md
• prompts/architecture-review.md
• prompts/assessment-quiz.md

Module Practice Files

Every module includes:

• README.md for concepts, tasks, tips, and completion criteria.
• exercises.md for hands-on practice.
• assessment.md for self-testing before moving on.

Definition of Done

The roadmap is complete when you can demonstrate:

• A working ML project with documented evaluation.
• A deep learning project with experiment tracking.
• A transformer or attention implementation from scratch.
• A RAG platform with citations, access control, and evaluation.
• An agent system with tool authorization and audit logs.
• An AI evaluation pipeline with regression reports.
• A containerized deployment with Docker and Kubernetes.
• A secure cloud architecture with monitoring, backups, cost estimates, and IaC.
• A product case study with KPIs, ROI, risks, and roadmap.
• A final portfolio package with architecture diagrams, ADRs, demos, and clear documentation.

Personal Rule

Do not measure progress by how many courses were completed.

Measure progress by what you can build, explain, secure, deploy, evaluate, and justify.