Project Setup & Engineering
Foundational engineering practices every ML/AI Engineer is expected to know: how to set up a project on GitHub the right way, and how to structure real ML, deep-learning, LLM, agent, and data-engineering projects.
These topics are deliberately "boring", and that's exactly why they show up in interviews and on the job. Strong modeling skills with weak engineering hygiene is the most common reason good prototypes never ship.
Guides
- How to Set Up a Project on GitHub: end-to-end tutorial: git config, repos, branching, pull requests, branch protection, CI with GitHub Actions, releases, GitHub Pages, secrets, and authentication.
- ML/AI Project Folder Structures: recommended directory layouts for classic ML, deep learning, LLM/GenAI apps, AI agents, and data-engineering pipelines, plus a "where does this go?" decision table and anti-patterns.
How this connects to the rest of the repo
- CI/CD details → GitHub Actions
- Containerization → Docker
- Experiment tracking & registries → MLflow
- Production AI engineering → LLMOps / MLOps Engineering
- Agent architecture → Agentic AI
Guides in This Track
Every guide in project_setup/. Start with the overview above, then work through these.