Career transition
Software Engineer to ML Engineer
Software engineers bring the discipline ML projects often need. The new work is data, model evaluation, and training-serving boundaries.
What already transfers
- Programming
- Testing
- Deployment
- System design
- Code review
Likely gaps
- Feature engineering
- Model training
- Metrics
- Data leakage
- ML deployment patterns
Do not waste time relearning
- Intro coding
- Generic web-app tutorials
Suggested progression
- ML fundamentals
- Data pipelines
- Training and validation
- Serving and monitoring
- ML systems project
Readiness evidence
- A trained model with a clear metric
- A repeatable pipeline
- Monitoring for drift or failure
Related learning
Turn this into today's learning plan.
Roe turns an outcome into the next useful lesson, then checks and reviews what sticks.
