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Professional Certificate
MLOps & AI Deployment
Keep models and LLM services healthy after launch.
- Length
- 3 or 6 months
- Level
- Intermediate
- Weekly
- 6 to 10 hours
- Format
- Live cohort, Self-paced
- Modules
- 6 to 11
- Next intake
- To be announced
What you'll learn
For engineers who need to take models from a notebook to dependable production systems. You learn to package, serve, monitor and retrain models, then extend those practices to LLM services where cost and latency behave differently.
By the end, you will be able to
- Package models reproducibly with pinned environments and artifacts
- Serve models behind versioned APIs in containers
- Automate training and deployment with CI/CD
- Detect data and concept drift and trigger retraining
- Operate LLM services with caching, routing and cost controls
Curriculum
Switch between the 3- and 6-month options to see how the program expands. Every module includes a hands-on lab.
6 modules, 12 weeks, about 72 to 120 study hours
Core modulesCapstone
Topics
- Dependency pinning
- Artifact storage
- Data versioning
Lab
Rebuild a colleague's model bit-for-bit
Estimated effort
12 to 20 hours over 2 weeks
Skills you'll build
- Model servingAdvanced
- CI/CD for MLAdvanced
- MonitoringAdvanced
- KubernetesProficient
- LLMOpsProficient
Tools and technologies
- Docker
- Kubernetes
- MLflow
- GitHub Actions
- FastAPI
- Prometheus
- Grafana
- vLLM
Product names are trademarks of their owners, listed only to describe what we teach. No endorsement is implied.
Portfolio projects
Representative projects from the curriculum. Your capstone is scoped with a mentor around a problem you choose.
Gated model release
A CI pipeline that trains, tests and only promotes models that beat production.
MLflow, GitHub Actions
Drift alarm
Monitor live predictions and alert when input distributions shift.
Prometheus, Grafana
6-month option
Cost-aware LLM gateway
Route requests across models with caching and per-team budgets.
vLLM, Kubernetes
How it's delivered
- Live cohort
- Scheduled live classes each week with your cohort, recorded for different time zones, plus self-paced lessons and labs.
- Self-paced
- Study on your own schedule with deadlines for assessed work and booked mentor office hours.
Instructors
Instructor and mentor profiles for this program are published once each person's background has been verified. We don't show placeholder faces or borrowed credentials.
Admissions
- 1Submit an online application
- 2Readiness review by admissions
- 3Decision by email
- 4Confirm your place and start onboarding
Tuition
- 3-month optionPublished before intake
- 6-month optionPublished before intake
Payment options vary by country and are shown at checkout.
Tuition and payment optionsCertificate
Complete every required module, pass the assessments and the capstone review to earn the Professional Certificate.
Each certificate has a unique ID and QR code that anyone can check on our verification page.
This is a professional credential issued by Zyvora Academy, not an academic degree.
Where it can lead
Roles this program builds toward. Outcomes depend on your background, effort and local job market; we don't guarantee employment.
Machine Learning Engineer
Turns data into models and models into dependable services.
Role detailsMLOps Engineer
Keeps models and AI services healthy in production.
Role details
Program questions
Syllabus
Get the full syllabus
We'll email the detailed syllabus, and the fee sheet as soon as tuition for the next intake is published.
Advisor
Speak to an advisor
Ask about fit, prerequisites or which length suits you. An advisor replies by email to arrange a call.
