Machine Learning Engineer Path
Software and data professionals who want to build, train, and ship machine learning models to production.
4 stepsHands-on labsCapstone + portfolioA named role
Save $147 vs. courses separately
By the end, you can
What this path makes you able to do.
- Frame an ML problem and engineer features from raw data
- Build, train, tune, and validate models without overfitting
- Deploy and serve models and monitor them for drift
A guided journey, not a pile of courses
The 4-step path.
- 1View course
Machine Learning Engineering Foundations
The ML lifecycle and features
- 2View course
Building & Training ML Models
Train, tune, and validate
- 3View course
Deploying & Serving ML Models
Serve and monitor for drift
Capstone: build and deploy an ML model
A trained and validated model with a serving API and a drift-monitoring plan
Every path is backed by
Hands-on labs. A final exam & capstone. A real role.
You finish with a portfolio you can show, a cumulative exam, and the skills to land the Machine Learning Engineer role.
Not sure this is the one? Take the free fit and aptitude check first: twelve short questions, instant answer.