Machine Learning Engineer Skills Checklist
Takes models from notebook to reliable production systems. Tick the 7 core skills employers expect — like Python, software engineering and testing, Machine learning fundamentals, Deep learning with PyTorch — and the extras that set you apart, then see your readiness and what to learn next.
Core
0/5MLOps
0/6Machine Learning Engineer readiness
0%
0 of 7 core skills · 0 of 11 overall
Start with the core skills — they're what job posts and interviews expect first.
Learn next
1. Python, software engineering and testing
Package a model as a tested Python library.
2. Machine learning fundamentals
Train, evaluate and tune models with scikit-learn.
3. Deep learning with PyTorch
Train and fine-tune neural networks.
Your ticks aren't saved — this checklist runs only in your browser.
How to use the Tech Skills Checklist
- 1Open the checklist for the role you're aiming for — from this page or the Free tools menu.
- 2Tick the skills you already have.
- 3Read your readiness score and the next three skills to learn, then browse matching jobs.
Frequently asked questions
Which roles are covered?
Data analyst, BI, product and AI analyst, data engineer, data scientist, ML engineer, full stack, frontend, backend, mobile, QA, AWS, Google Cloud, Azure, DevOps, cybersecurity, product manager and UI/UX designer.
What's the difference between core and other skills?
Core skills are what most job posts and interviews expect. The rest set you apart, or matter in some teams more than others.
Are my ticks saved?
No — the checklist runs only in your browser and isn't stored.
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