Statements (113)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Model
gptkb:open-source_software |
| gptkbp:built |
gptkb:Kubernetes
Kubernetes resources |
| gptkbp:constructed_in |
high availability
|
| gptkbp:developed_by |
gptkb:Go_programming_language
gptkb:Kubeflow_community |
| gptkbp:enables |
canary deployments
real-time predictions logging and monitoring serverless inference |
| gptkbp:has |
community support
custom resource definitions active contributors |
| gptkbp:hosted_by |
gptkb:Git_Hub
|
| https://www.w3.org/2000/01/rdf-schema#label |
KFServing
|
| gptkbp:integrates_with |
gptkb:Kubernetes
Istio for traffic management |
| gptkbp:is_available_on |
gptkb:Git_Hub
cloud platforms |
| gptkbp:is_compatible_with |
gptkb:Kubeflow_Pipelines
various data sources data pipelines CI/ CD pipelines |
| gptkbp:is_designed_for |
cloud-native applications
production environments |
| gptkbp:is_designed_to |
reduce operational complexity
simplify model deployment |
| gptkbp:is_documented_in |
official documentation
Kubeflow documentation |
| gptkbp:is_effective_against |
real-time inference
|
| gptkbp:is_focused_on |
model serving efficiency
|
| gptkbp:is_integrated_with |
logging tools
monitoring tools alerting systems Kubeflow Training |
| gptkbp:is_open_source |
gptkb:true
|
| gptkbp:is_optimized_for |
low-latency inference
Kubernetes environments |
| gptkbp:is_part_of |
MLOps practices
Kubeflow ecosystem AI/ ML solutions AI/ ML workflows cloud-native AI solutions MLOps workflow |
| gptkbp:is_promoted_by |
meetups
hackathons Kubeflow conferences |
| gptkbp:is_scalable |
gptkb:true
large datasets |
| gptkbp:is_supported_by |
workshops
community forums webinars online tutorials major cloud providers cloud-native technologies Kubernetes operators |
| gptkbp:is_tested_for |
unit tests
integration tests |
| gptkbp:is_used_by |
data scientists
ML engineers machine learning engineers Dev Ops teams |
| gptkbp:is_used_for |
real-time analytics
AI applications model deployment model management model serving |
| gptkbp:is_used_in |
enterprise applications
production environments |
| gptkbp:is_utilized_by |
research institutions
startups |
| gptkbp:offers |
automatic scaling
|
| gptkbp:provides |
predictive analytics
automatic scaling model versioning logging and monitoring traffic splitting canary rollouts model health checks model rollback capabilities model serving lifecycle management model serving templates predictive serving capabilities preprocessing and postprocessing capabilities RESTful API for inference |
| gptkbp:purpose |
serving machine learning models
|
| gptkbp:suitable_for |
large-scale deployments
|
| gptkbp:supports |
gptkb:Tensor_Flow
gptkb:g_RPC gptkb:Py_Torch gptkb:Oni gptkb:Scikit-learn data preprocessing GPU acceleration REST APIs custom metrics multiple frameworks model monitoring ONNX models multiple machine learning frameworks batch predictions Scikit-learn models multi-model serving A/ B testing Tensor Flow models Py Torch models g RPC for inference |
| gptkbp:uses |
gptkb:Knative
Kubernetes for deployment |
| gptkbp:written_in |
gptkb:Go
|
| gptkbp:bfsParent |
gptkb:Kubeflow
|
| gptkbp:bfsLayer |
5
|