MLOps and AI Model Deployment
Deploy a versioned synthetic-data model with monitored promotion and rollback.
MLOps and AI Model Deployment develops practical capability through six curriculum modules combining focused lessons, guided activities and assessed deliverables. Deploy a versioned synthetic-data model with monitored promotion and rollback. The six-week outline is an estimated learning sequence, not a confirmed cohort timetable; tutors and administrators configure actual dates separately.
Learning outcomes
- Define a model lifecycle plan.
- Track a reproducible experiment.
- Serve a validated model endpoint.
- Build a gated model promotion flow.
- Simulate model and service degradation.
Who it's for
ML engineers and platform teams
Prerequisites
Machine learning, Python and containers
Learning tools
Local model registry, container tools and test service
Estimated learning time
48 hours · 6 curriculum modules
Curriculum
Final project
Deploy a versioned synthetic-data model with monitored promotion and rollback.
Completion & certificates
- Complete each published lesson.
- Submit every practical assignment and the final project.
- Satisfy the published assessment criteria in tutor or administrator review.
- Receive administrator completion review before certificate eligibility.
Certificate eligibility requires verified completion and administrator review. No certificate has been issued for this course.
Cohorts & class dates
No cohort dates have been confirmed. The curriculum sequence is not a scheduled class timetable.