MLOps engineer
Run and operate ML models in production
Who is an MLOps engineer?
MLOps engineers industrialize ML—automated training, versioning, deployment, and monitoring bridging data science and infrastructure cultures.
The role
- Codifies CI/CD arcs from experiments to promotions.
- Monitors inference pipelines and drift proactively.
Fit in the team
- Bridges DS creativity with SRE rigor—no informal tarball drops.
- Aligns compliance on datasets and artifact access boundaries.
- Works with security on signed artifacts before production rollout.
Skills that matter early
- Kubernetes and Helm for serving—with controlled canary rollouts.
- GPU training job packaging—for believable capacity forecasts.
- MLflow, Kubeflow, or equivalents—experiment registries teams trust.
- Model plus data versioning—rollback paths exist before incidents escalate.
- Inference observability—you catch regressions before support spikes.
- Terraform or Pulumi when applicable—infra changes stay reviewable.
How learning works
Short lessons and hands-on practice at your level—from fundamentals to tasks close to real work.
Getting started
Sign up, explore the interface, and take the skills check—we capture your baseline and starting point.
Personal plan
A path tailored to your chosen track: topics, module order, and practical assignments.
Theory and practice together
After each theory block—tasks at your level, from drills to connected work you can show employers.
Feedback
Automated checks where they fit; otherwise breakdowns of mistakes and hints for what to do next.
Your pace
Study anytime; revisit harder topics until you’re confident.
Close to the job
Focus on workflows and deliverables typical for the role—tasks, collaboration, and clear progress updates.
Ready to start learning?
Register on the platform and get a personalized learning plan for this profession.
Try for free. No card required.