CS and software: ML domain track
You already write code and think in systems. This track is the thin overlay that says you are on the default Road to ML spine. Use full module coverage for your target role. Do not treat this folder as a shortcut that skips hard evaluation work.
Core idea. Follow Modules 00–25 as the primary path. Pair with system design and full-stack resources when you build products. Role tables in the main README already encode emphasis.
Who this is for
- CS students and professional developers entering ML
- People targeting ML Engineer, AI Engineer, or LLM-oriented roles
- Learners who already know they want the technical role cards on the Study Hub
What you already bring
- Programming fluency
- Debugging and version control habits
- Systems intuition (APIs, data stores, failure)
ML problem types you will meet
- The full supervised and deep learning catalog in this repo
- Production ML and MLOps
- Optional GenAI, CV, NLP, and RL by role
- System design for ML services
Gaps this track closes
Most gaps are role-shaped, not domain-shaped. Use the main Career Paths table.
Typical spine:
- Module 00–01
- Modules 02–05
- Role-specific modules from the Career Paths table
- Module 13–14 for production roles
- System Design and ML System Design Guide for service thinking
- Optional Full-Stack AI Track when you build product surfaces
Related resources
- ML System Design Guide. Service thinking for ML products.
- DSA for ML Guide. Algorithm habits that still matter in ML roles.
- Git Guide. Collaboration and review habits for portfolio work.
- Docker Tutorial. Containers for repeatable demos and services.
- MLOps Cheatsheet. Quick sheet beside Modules 13–14.
- Backend Engineering Roadmap. APIs and data stores beside ML work.
- Ethics in ML. Product features still need harm awareness.
Role emphasis (not destiny)
| Emphasis | Role | Why |
|---|---|---|
| Primary | ML Engineer | Production ML fits software backgrounds |
| Alternate | AI Engineer | Broader catalog including later modules |
Also consider LLM Engineer or GenAI Solution Architect when language systems are the job. See Career Roadmap Guide. Treat times as emphasis maps only. This track does not guarantee a hire, visa, or role outcome.
Intensity maps
Research support (tier B)
Use the Data Scientist or Research Scientist module lists in the main README. Prefer evaluation and explainability before novelty chasing.
Job-oriented study (tier C)
- Follow the ML Engineer module list in Career Paths
- Add system design foundations
- Ship projects from Modules 16–18 with README-quality evidence
- Add full-stack chapters only if the role needs product engineering
Ordered study checklist
- Pick a role in the main Career Paths table
- Finish Modules 00–05 without skipping Module 05
- Follow the role module list in order
- Build one portfolio project with clear metrics and failure notes
- Add deployment or GenAI modules only when the role needs them
Start here
Module 00 README or Module 01 README if prerequisites are already solid.
Honesty and traps
- Software skill does not replace statistical humility.
- Wrapping an API is not the same as shipping evaluated ML.
- This track stays thin on purpose. Other domains need more bridge prose. You need disciplined module coverage.
Try next: Open the Career Paths table, pick ML Engineer or AI Engineer, and start the first module in that list.