Statements (59)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:software
|
| gptkbp:allows |
model versioning
|
| gptkbp:constructed_in |
open-source technologies
|
| gptkbp:deployment |
models as microservices
|
| gptkbp:developed_by |
gptkb:Seldon
|
| gptkbp:enables |
canary deployments
automated rollbacks |
| gptkbp:facilitates |
scalability
|
| gptkbp:features |
API management
|
| gptkbp:has |
community support
|
| https://www.w3.org/2000/01/rdf-schema#label |
Seldon Deploy
|
| gptkbp:integrates_with |
gptkb:fortification
CI/ CD tools |
| gptkbp:is_available_in |
gptkb:Seldon_Core
Seldon Deploy documentation |
| gptkbp:is_available_on |
cloud platforms
Seldon website |
| gptkbp:is_compatible_with |
gptkb:lake
gptkb:Graphics_Processing_Unit gptkb:Kubeflow gptkb:scikit-learn gptkb:Py_Torch M Lflow |
| gptkbp:is_designed_for |
data scientists
ML engineers Dev Ops teams |
| gptkbp:is_documented_in |
gptkb:archive
|
| gptkbp:is_open_source |
gptkb:theorem
|
| gptkbp:is_optimized_for |
high availability
|
| gptkbp:is_part_of |
gptkb:Seldon_platform
AI deployment solutions ML Ops ecosystem |
| gptkbp:is_promoted_by |
tutorials
blog posts case studies webinars Seldon team |
| gptkbp:is_scalable |
large datasets
|
| gptkbp:is_supported_by |
Seldon community
Seldon partners |
| gptkbp:is_used_by |
enterprises
|
| gptkbp:is_used_for |
real-time predictions
model monitoring model lifecycle management |
| gptkbp:is_used_in |
production environments
|
| gptkbp:offers |
gptkb:operating_system
A/ B testing |
| gptkbp:provides |
performance metrics
customizable dashboards monitoring capabilities security features logging features |
| gptkbp:purpose |
model deployment
|
| gptkbp:supports |
data privacy compliance
machine learning models model explainability multi-model serving |
| gptkbp:bfsParent |
gptkb:Seldon_Ecosystem
|
| gptkbp:bfsLayer |
6
|