MLOps Engineering with Aryan

Master Docker, Kubernetes, CI/CD, MLflow and model deployment in 12 weeks

24 modules120 lessons25 hoursIntermediate level
4.8 (127 reviews)

Enroll in this course

  • All 24 modules
  • AI trainer sessions
  • Quizzes and assignments
  • Certificate
  • Job support (1-year only)
Enroll now

Secure payment via Stripe. 7-day refund policy.

What you will learn

Containerize ML models with Docker
Orchestrate deployments with Kubernetes
Build CI/CD pipelines for ML workflows
Track experiments and models with MLflow
Monitor model performance and detect drift in production
Automate retraining pipelines end-to-end
Design scalable, production-ready model-serving APIs
Apply the MLOps practices real platform teams use daily

Meet your trainer

AR

Aryan

AI Trainer · MLOps

Aryan spent six years building and maintaining ML infrastructure for fintech and healthcare platforms before becoming a full-time AI trainer. He's deployed models that process millions of predictions a day — and debugged what went wrong when they didn't.

Teaching style: Patient and methodical. Aryan breaks every pipeline into steps you can actually follow, and won't move on until the fundamentals are solid.

Docker & KubernetesCI/CD for MLMLflow & experiment trackingModel monitoringProduction debugging
Chat with Aryan

Course curriculum

12 weeks · 24 modules · 120 lessons

Module 1: Docker fundamentals

5 lessons · 1h 30m

Module 2: Docker in production

5 lessons · 1h 15m

Reviews

4.8

127 reviews

5 star
78%
4 star
15%
3 star
5%
2 star
1%
1 star
1%

“The pacing was perfect — Aryan didn't move to Kubernetes until I actually understood Docker networking.”

Daniel Osei

ML Engineer

“I've taken MLOps courses on other platforms before. This is the first one where someone actually checked I understood before moving on.”

Priyanka Rao

Data Scientist

“Worth it just for the CI/CD pipeline module. I used what I learned directly on our team's deployment pipeline.”

Tom Bennett

DevOps Engineer