Kubeflow
End-to-end ML pipelines on Kubernetes — training, tuning, serving.
Maturity level: L3 — Can Deploy
Six perspectives on Kubeflow
Roadmap
Advanced MLOps — after K8s, Helm, and MLflow.
Architecture
Platform layer for ML pipelines on Kubernetes.
Company
Platform engineering and senior MLOps at scale.
Projects
Full pipeline on Kubeflow Pipelines.
Interview
ML platform design.
Career
AI platform engineer path.
What & Why
What: Kubernetes-native platform for ML workflows, pipelines, and Katib hyperparameter tuning.
Why: Large teams standardize on Kubeflow for multi-tenant ML platforms on K8s.
Build this
Kubeflow pipeline from data ingest → train → evaluate → deploy via KServe.
Production reality
- ! Complex upgrades
- ! Resource contention
- ! Steep learning curve
Interview preparation
- Kubeflow vs MLflow
- When is Kubeflow worth the complexity?
Connected skills
Explore Kubeflow in the interactive universe or train with live cohorts.
Want this Kubeflow learning path turned into your own plan?
A roadmap shows the destination. In 1:1 mentorship I turn it into a week-by-week learning path for your background, review your projects, and prepare you for interviews until you land the role. Online, in your time zone.
Start with one 60-minute 1:1 call: .
Prefer a structured live course? Join the MLOps · AIOps · LLMOps · Agentic AI masterclass →