track · ship
DevOps/MLOps Track
Most training programs bury MLOps as a bullet point inside a DevOps course. Here, it's its own curriculum — because deploying and running AI systems is a different job than deploying web apps.
› stage/01/foundationsDevOps Foundations
- Linux fundamentals and shell scripting
- Docker and containerization
- Kubernetes orchestration
- CI/CD pipelines (GitHub Actions, Jenkins)
- Infrastructure as Code with Terraform
- Cloud fundamentals on AWS
› stage/02/mlopsMLOps — Its Own Track, Not an Elective
A model that never reaches production is worthless. This is the job that gets it there and keeps it running:
- Packaging and serving trained models as APIs
- ML pipeline orchestration end to end
- Experiment tracking and model versioning (MLflow)
- Model monitoring and drift detection in production
- Data versioning alongside model versioning
› stage/03/excelExcel
- 3 mock interviews for DevOps Engineer and MLOps Engineer roles, with recorded feedback
- Resume and LinkedIn rebuilt for the specific role you're targeting
- A real deployed project you can walk an interviewer through
Choose Your Format
Self-paced learners who want a personalized schedule.
- Full curriculum
- Weekly 1:1 sessions
- Paced to you
Learners who want peer accountability on a fixed schedule.
- Full curriculum
- Structured batch sessions
- Peer projects
Learners who want full support through to an offer.
- 1:1 or Cohort format
- Full placement support
- Interview prep
Includes full placement support and interview prep.