|
gptkbp:instance_of
|
gptkb:software_framework
|
|
gptkbp:bfsLayer
|
4
|
|
gptkbp:bfsParent
|
gptkb:Kubeflow
gptkb:Google_Cloud_AI_Platform_Pipelines
gptkb:Google_Cloud_AI_Platform_Pipelines_Jobs
|
|
gptkbp:allows
|
Reusability of components
|
|
gptkbp:can_be_extended_by
|
gptkb:battle
Custom components
|
|
gptkbp:community_support
|
gptkb:battle
|
|
gptkbp:deployment
|
On-premises environments
Various cloud platforms
|
|
gptkbp:design
|
gptkb:battle
|
|
gptkbp:developed_by
|
gptkb:Job_Search_Engine
|
|
gptkbp:enables
|
CI/ CD for ML models
|
|
gptkbp:features
|
Experiment tracking
Parameterization of pipelines
Visualization of pipeline runs
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Kubeflow Pipelines
|
|
gptkbp:includes
|
UI for managing pipelines
|
|
gptkbp:integrates_with
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
gptkb:park
|
|
gptkbp:is_compatible_with
|
gptkb:Kubeflow_Katib
gptkb:Kubeflow_Pipelines_SDK
gptkb:Kubeflow_Serving
Kubeflow Training
|
|
gptkbp:is_designed_for
|
Building and deploying machine learning workflows
|
|
gptkbp:is_documented_in
|
Kubeflow documentation
|
|
gptkbp:is_open_source
|
gptkb:theorem
|
|
gptkbp:is_part_of
|
Kubeflow ecosystem
|
|
gptkbp:is_scalable
|
gptkb:battle
|
|
gptkbp:is_supported_by
|
Kubernetes community
Open source contributors
|
|
gptkbp:is_used_by
|
Data scientists
ML engineers
Dev Ops teams
|
|
gptkbp:is_used_for
|
Model evaluation
Model training
Model deployment
|
|
gptkbp:latest_version
|
gptkb:battle
|
|
gptkbp:operational_area
|
gptkb:battle
|
|
gptkbp:provides
|
RESTAPI
Pipeline orchestration
Logging and monitoring capabilities
Component marketplace
SDK for Python
|
|
gptkbp:supports
|
Data versioning
Custom components
Hyperparameter tuning
Containerized applications
Distributed training
Artifact management
Multi-step workflows
|
|
gptkbp:tutorials
|
gptkb:battle
|
|
gptkbp:uses
|
gptkb:fortification
|
|
gptkbp:written_in
|
gptkb:Library
gptkb:Go
|