|
gptkbp:instance_of
|
gptkb:software_framework
|
|
gptkbp:bfsLayer
|
5
|
|
gptkbp:bfsParent
|
gptkb:Kubeflow_Pipelines
|
|
gptkbp:allows
|
Model versioning
|
|
gptkbp:can_be_extended_by
|
gptkb:theorem
|
|
gptkbp:constructed_in
|
Kubernetes architecture
|
|
gptkbp:deployment
|
Cloud platforms
On-premises infrastructure
|
|
gptkbp:developed_by
|
gptkb:Job_Search_Engine
gptkb:Go_programming_language
gptkb:language
|
|
gptkbp:enables
|
Canary deployments
A/ B testing
|
|
gptkbp:has
|
gptkb:operating_system
gptkb:Community_support
API endpoints
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Kubeflow Serving
|
|
gptkbp:integrates_with
|
gptkb:fortification
CI/ CD pipelines
|
|
gptkbp:is_available_on
|
gptkb:archive
|
|
gptkbp:is_compatible_with
|
gptkb:lake
gptkb:Helm
gptkb:Kubeflow_Pipelines
|
|
gptkbp:is_designed_for
|
Deploying machine learning models
|
|
gptkbp:is_documented_in
|
Git Hub repositories
Kubeflow documentation
|
|
gptkbp:is_open_source
|
gptkb:theorem
|
|
gptkbp:is_optimized_for
|
High availability
Low latency
|
|
gptkbp:is_part_of
|
Kubeflow ecosystem
Data pipeline management
ML Ops
AI/ ML workflows
|
|
gptkbp:is_scalable
|
gptkb:theorem
|
|
gptkbp:is_supported_by
|
Open source community
Cloud providers
|
|
gptkbp:is_tested_for
|
Unit tests
Integration tests
|
|
gptkbp:is_used_by
|
Data scientists
Machine learning engineers
|
|
gptkbp:is_used_for
|
Real-time inference
Model deployment
Model serving orchestration
|
|
gptkbp:is_used_in
|
Production environments
|
|
gptkbp:offers
|
RESTAPI
|
|
gptkbp:provides
|
Automatic scaling
Monitoring tools
Logging capabilities
Model serving capabilities
Custom model serving options
|
|
gptkbp:supports
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
Batch predictions
Model monitoring
Model artifacts
Multiple frameworks
Online predictions
|
|
gptkbp:uses
|
g RPC for communication
|