Career roadmap
ML Engineer Roadmap
Python and data fundamentals through model training, deployment, and MLOps — engineering first, hype last.
Timeframes assume a typical part-time pace. Faster or slower is fine — the order is what matters.
Python & math footing
Weeks 1–7Fluent Python plus just enough linear algebra, probability, and statistics to read what models are doing.
- You can clean, reshape, and explore a messy dataset
- Notation in ML papers stops being decorative
Working with data
Weeks 8–13Most of the job: acquiring, validating, and transforming data, with honest evaluation splits from day one.
- You can build a reproducible dataset pipeline
- You can spot leakage before it flatters your metrics
Classical ML
Weeks 14–19Regression, trees, ensembles, clustering — with scikit-learn — and the evaluation literacy to compare them honestly.
- You can take a tabular problem end to end and defend the metric
- You reach for the simplest model that works, on purpose
Deep learning & deployment
Weeks 20–27PyTorch fundamentals, transfer learning, and serving a model behind a real API instead of a notebook.
- You can fine-tune a pretrained model on your own data
- Your model answers HTTP requests with measured latency
MLOps & portfolio
Weeks 28–32Versioned data, tracked experiments, monitored models — packaged into two projects that show engineering, not just accuracy.
- Two deployed ML projects with tracked experiments and honest writeups
- You can discuss failure modes, not just leaderboard numbers
Keep reading
The developer's guide to ATS resumes
Most developer resumes die in applicant tracking systems before a human sees them. How parsing actually works, and how to write bullets that survive both filters.
The 7 code dimensions that decide your hireability score
DevProfile scores every analysis across seven dimensions of your actual code. What each one measures, why reviewers care, and how to raise it.
Get the version of this roadmap built from your repos
DevProfile analyzes your code, finds where you actually are on this path, and orders the next steps by score impact.