Engineering Practices & Tooling
The Missing Semester for AI Engineers — git, the command line, APIs, and the practical craft real jobs assume you already know
8 phases. 32 lessons. 32 labs. 1 capstone. The professional-practice half of this platform's zero-background entry point — the command line, git for real collaboration, dependency and environment management, how the internet and APIs actually work, SQL, systematic debugging, and cloud deployment, all applied to an AI-engineering context throughout. Ends with a real, deployed web service.
- Lessons: —
- Labs: —
- Projects: —
- Level: Beginner
Curriculum
- Command Line & OS Literacy — Filesystem navigation, piping small tools together, permissions and $PATH, job control and signals.
- Git for Real Work — Commits as snapshots, branching and merging, resolving real conflicts, commit messages that serve a future reader.
- Dev Environments & Dependency Management — Why 'works on my machine' fails, uv and real lock files, Docker image layers and build caching.
- How the Internet & APIs Actually Work — DNS through HTTP, reading unfamiliar API docs, async/await, exponential backoff and idempotency.
- Databases & SQL for AI/ML — SQL's real execution order, joins for training datasets without silent row loss, window functions, NoSQL vs. relational.
- Debugging Methodology — The escalation ladder from print to debugger, reading stack traces, hypothesize-predict-test-observe, git bisect.
- Cloud & Deployment Concepts — IaaS/PaaS/SaaS, environment-variable-driven config, deploying a real container to a real, current cloud platform.
- Capstone — Designing, building, and deploying a real small REST service — git-tracked, tested, containerized, and publicly reachable.
Skills You Will Learn
- Git & Version Control
- Command Line Proficiency
- Docker & Environments
- REST APIs
- SQL
- Systematic Debugging
- Cloud Deployment
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