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Engineering Practices & Tooling

The Missing Semester for AI Engineers: git, the command line, APIs and the craft real jobs assume you have

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.

8 phases · 32 lessons · 32 labs · 1 capstone · Level: Beginner

Take Programming & CS Foundations first — this course builds on it.

Outcomes you will have by the end

• A real, deployed REST service — A FastAPI + database service, git-tracked from commit one, tested, containerized, and deployed to a genuinely reachable public URL — not a local-only exercise.
• Real git collaboration skills — The ability to resolve a genuine merge conflict, write commit messages a future reader can actually use, and answer 'which exact code produced this result' with certainty.
• Working knowledge of the full deployment path — From a local script to a containerized, environment-configured, publicly deployed service — the professional-practice gap most 'learn to code' courses skip entirely.
• Verified certificate of completion — Issued by DeVenture Academy, tied to your completion record. Lists the git, tooling, API, database, and deployment skills you built.

What you will be able to do

Git & Version Control · Command Line Proficiency · Docker & Environments · REST APIs · SQL · Systematic Debugging · Cloud Deployment

Every phase, every lesson, every project

• Command Line & OS Literacy (4 lessons) — free — Filesystem navigation, piping small tools together, permissions and $PATH, job control and signals.
  • Navigating Without a Mouse
  • Reading, Filtering, and Combining Text
  • Permissions, Processes, and Environment Variables
  • Job Control, Signals, and Making the Terminal Yours
• Git for Real Work (4 lessons) — free — Commits as snapshots, branching and merging, resolving real conflicts, commit messages that serve a future reader.
  • What Git Actually Tracks
  • Branching and Merging for Real Work
  • Commit Hygiene and Code Review
  • Git for Experiment Tracking
• Dev Environments & Dependency Management (4 lessons) — Why 'works on my machine' fails, uv and real lock files, Docker image layers and build caching.
  • "Works on My Machine"
  • Virtual Environments and Dependency Management
  • Containers: What They Actually Are
  • Editors, Environments, and Real Workflow
• How the Internet & APIs Actually Work (5 lessons) — DNS through HTTP, reading unfamiliar API docs, async/await, exponential backoff and idempotency.
  • What Actually Happens When You Load a Page
  • REST APIs and Reading Docs Cold
  • Why Blocking I/O Is a Problem
  • async/await in Practice
  • Rate Limits, Retries, and Real-World API Etiquette
• Databases & SQL for AI/ML (5 lessons) — SQL's real execution order, joins for training datasets without silent row loss, window functions, NoSQL vs. relational.
  • Why Not Just Use a File?
  • SQL: Getting Data Out
  • Joins and Building Training Datasets
  • Window Functions
  • NoSQL and Schema Design
• Debugging Methodology (4 lessons) — The escalation ladder from print to debugger, reading stack traces, hypothesize-predict-test-observe, git bisect.
  • The Cheapest Tool First
  • Reading a Stack Trace
  • Systematic (Scientific) Debugging
  • Binary Search Debugging
• Cloud & Deployment Concepts (3 lessons) — IaaS/PaaS/SaaS, environment-variable-driven config, deploying a real container to a real, current cloud platform.
  • What "The Cloud" Actually Is
  • Environment Variables and Config, for Real
  • Actually Deploying Something
• Capstone (3 lessons) — Designing, building, and deploying a real small REST service — git-tracked, tested, containerized, and publicly reachable.
  • Design the Service
  • Build It
  • Ship It

The technologies you will use

Git · Docker · uv · SQLite · FastAPI

Roles this course prepares you for

• Junior DevOps / Platform Engineer ($70k–$95k) — Manage environments, containers, and deployments — built directly by this course's dependency management, Docker, and cloud-deployment phases.
• Junior Backend / API Developer ($70k–$100k) — Build and consume REST APIs, work with SQL databases, and debug production issues — the direct focus of this course's API, database, and debugging phases.

Common questions

I already know how to program — do I still need Programming & CS Foundations first?

No — if you already write correct Python and understand functions, loops, and classes, you can start directly here. This course assumes that and builds the professional practice most 'learn to code' paths skip.

Why does this course exist separately from the AI-focused courses?

Because git, Docker, SQL, and deployment aren't AI-specific skills — they're the general engineering practice every AI course on this platform assumes you already have, the same gap MIT's own 'Missing Semester' was created to fill.

Is the capstone a real deployment, or a simulation?

Real — you deploy an actual containerized service to a real, current cloud platform with a genuinely reachable public URL, not a local-only exercise.

Continue your learning path

Programming & CS Foundations · Data Analysis & Visualization · ML & AI Engineering

Start the Engineering Practices & Tooling course

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