From Software Engineer to ML Engineer
Use your engineering strengths to move into ML engineering with a focused plan for maths and ML
A taste of a lesson
I'm a backend engineer. Should I spend a year on maths before touching ML?
No need for a year. Target the maths ML uses: vectors and matrix multiplication, derivatives and the chain rule, and basic probability. A few weeks of focused practice, alongside ML, works better than maths in isolation. Implement linear regression with gradient descent yourself and you'll use all three. Meanwhile your backend skills already matter for serving and pipelines. How comfortable are you with derivatives right now: rusty, okay or confident?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Map your engineering strengths to ML engineering needs
- Plan a targeted maths and ML fundamentals refresh
- Build an end to end ML project with deployment and monitoring
- Explore internal transfer routes and explain your transition in interviews
Lesson plan
- 1 Strengths and gaps Audit what you bring and what ML engineering roles require. Start
- 2 Targeted maths refresh Refresh the linear algebra, calculus and probability that ML actually uses. Start
- 3 ML fundamentals by building Build intuition by implementing simple algorithms before using libraries. Start
- 4 How ML differs from software Adapt testing, debugging and correctness thinking to statistical systems. Start
- 5 End to end project with MLOps Build a project covering data, training, serving and monitoring. Start
- 6 Transition strategy Plan internal or external moves and tell your transition story. Start
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About this tutor
For experienced software engineers who want to move into machine learning engineering. Your strengths in code quality, systems, testing and production are exactly what many ML teams lack, so we build on them. We plan the maths refresh you actually need, ML fundamentals, hands on modelling, and the MLOps side (pipelines, deployment, monitoring) where you can stand out. You design a project that shows end to end ML engineering, explore internal transfer options, and prepare to explain your transition in interviews. Honest about timelines and gaps.
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About the teacher
Machine learning engineer who runs portfolio reviews and mock interviews for technical AI roles
9 tutors 243 lessons taught Sample
I work as a machine learning engineer and have spent a lot of my spare time reviewing portfolios and running mock interviews for people trying to get into ML, data and AI product roles. I have sat on both sides of the interview table, so I know what interviewers listen for and how often strong people undersell themselves. I teach...
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