for the ❤️ of teaching
These come out of my own self-learning — inspired by the real struggles I ran into along the way. I'm publishing them here hoping they make someone else's journey a little smoother. But reading these isn't the work — you still have to sit with the hard parts, think them through on your own, and get your hands dirty applying it. Consider this a push in the right direction, not a shortcut around the effort. Go at whatever pace works for you — there's no clock running here.
Cloud & Infrastructure
A complete, practitioner-oriented guide through every AZ-104 exam domain — 25 lessons across 6 modules covering identity & governance, storage, compute, networking, monitoring, and enterprise-scale architecture. Built for engineers who want to understand the reasoning behind every Azure decision, not just the commands.
A complete, certification-aligned path through Kubernetes — 91 lessons across 14 chapters. From architecture and core workloads through networking, storage, scheduling, RBAC and advanced security, observability, CRDs and operators, service mesh and autoscaling, deep internals (API server, etcd, scheduler, kubelet), and production operations at scale. Tagged throughout for CKA, CKAD, and CKS relevance.
A complete, hands-on path through Docker — from the shipping-container analogy and the Linux primitives containers are built from, through Dockerfiles, Compose, networking, and observability, to production hardening, image supply-chain security, and an orchestration primer that hands off cleanly into Kubernetes.
A complete path through CI/CD — from your first GitHub Actions workflow through caching, secrets, and matrix builds, deploying to Azure App Service, ACR, and AKS, GitOps with ArgoCD and progressive delivery, supply-chain hardening with Cosign and OPA Gatekeeper, and operating pipelines reliably at scale.
A complete path to AZ-305 and professional architect skills — 50 lessons across 8 phases. Built for engineers who already know Azure administration (AZ-104) and want the architect layer on top: not how to create a VNet, but when, why, and at what tier. Covers governance, networking, compute, data platforms, security, reliability, advanced patterns, and closes with real case-study drills and a capstone architecture.
AI & Machine Learning
A structured, from-scratch path through machine learning and deep learning — built for engineers who want to understand the math and code, not just import sklearn. Six phases covering data science foundations, supervised and unsupervised ML, deep learning, NLP & LLMs, and production MLOps — 69 lessons in total.
A practical, project-driven curriculum for software engineers moving into AI engineering — calling LLM APIs, prompt engineering, building RAG pipelines end to end, tool-using and multi-agent systems, MCP, and shipping AI features safely with guardrails, cost control, and observability. Every lesson after the first builds something real.
Programming & Backend
A complete learning path from zero to production — structured, progressive, no gaps. Seven phases covering how the web works, REST APIs, databases, authentication, testing, Docker, CI/CD, and advanced system design. Built for aspiring engineers who want to understand the backend deeply, not just follow recipes.
A complete path through Python — from your very first print("Hello") to production-ready code. Covers core language fundamentals, intermediate patterns (decorators, generators, context managers), concurrency and type hints, building production APIs with FastAPI, applying Python to ML/AI with NumPy/Pandas/scikit-learn, and a bonus phase on CPython internals — bytecode, the object model, descriptors, metaclasses, and the import system.
As agentic coding tools take over more and more of the actual typing, the engineer's job is shifting — less time hand-writing implementation, more time deciding what the system should look like in the first place: the trade-offs, the failure modes, the architecture an AI assistant can't choose for you. This is a complete, from-scratch path through that discipline — 15 chapters and 65 lessons from first principles through databases, caching, consensus, microservices, and security, each chapter closing with a scored quiz and a set of interview questions with model answers.