MLOps Engineer Path
Engineers who want to automate and operate the machine learning lifecycle at production scale.
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.
- Apply MLOps practices and the maturity model to real ML systems
- Build automated pipelines with feature stores, tracking, and a model registry
- Operate models in production: monitoring, retraining, and governance
A guided journey, not a pile of courses
The 4-step path.
- 1View course
MLOps Foundations
MLOps and the maturity model
- 2View course
ML Pipelines & Automation
Pipelines, registries, CI/CD
- 3View course
Production ML Operations
Monitor, retrain, govern
Capstone: build an MLOps pipeline
An automated training-to-serving pipeline with experiment tracking, a model registry, and production monitoring
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 MLOps Engineer role.
Not sure this is the one? Take the free fit and aptitude check first: twelve short questions, instant answer.