AI / ML ML Pipeline stable
Kubeflow
ML toolkit for Kubernetes-native model training and serving
14.0K stars
600 contributors
Since 2018
End-to-end ML platform on Kubernetes with pipelines, hyperparameter tuning (Katib), model serving (KServe), Jupyter notebooks, and distributed training operators.
License
Apache-2.0
Min RAM
8 GB
Min CPUs
4 cores
Scaling
distributed
Complexity
expert
Performance
enterprise grade
Self-hostable
✓
K8s native
✓
Offline
✕
Pricing
fully free
Docs quality
good
Vendor lock-in
none
Use cases
- ✓ End-to-end ML pipelines on Kubernetes
- ✓ Distributed training across GPU clusters
- ✓ Hyperparameter search with Katib
- ✓ Model serving with KServe at scale
Anti-patterns / when NOT to use
- ✕ Requires Kubernetes — not for simple setups
- ✕ Complex installation and maintenance
- ✕ Overkill for single-model projects
- ✕ Steep learning curve
Integrates with
Compare with alternatives
Replaces / alternatives to
Technical specs
Language
PythonGo
API type
RESTSDK
Protocols
HTTP
Deployment
docker
SDKs
python
Community
GitHub stars 14.0K
Contributors 600
Commit frequency weekly
Plugin ecosystem none
Backing Google / CNCF
Funding foundation
Release
Latest version
— Last release —
Since 2018
Best fit
Team size
mediumenterprise
Industries
enterpriseresearchfintech