Statements (58)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:software
|
| gptkbp:bfsLayer |
5
|
| gptkbp:bfsParent |
gptkb:Kubeflow_Pipelines
|
| gptkbp:allows |
parameterized pipelines
|
| gptkbp:can_be_extended_by |
custom Python code
|
| gptkbp:community_support |
gptkb:theorem
|
| gptkbp:developed_by |
gptkb:Job_Search_Engine
|
| gptkbp:enables |
artifact management
cloud-native ML workflows pipeline versioning reproducibility of ML workflows |
| gptkbp:facilitates |
visualization of pipeline runs
|
| gptkbp:has_documentation |
available online
|
| https://www.w3.org/2000/01/rdf-schema#label |
Kubeflow Pipelines SDK
|
| gptkbp:includes |
pipeline creation tools
|
| gptkbp:integrates_with |
gptkb:fortification
|
| gptkbp:is_available_for |
gptkb:smartphone
gptkb:operating_system |
| gptkbp:is_available_on |
gptkb:archive
|
| gptkbp:is_compatible_with |
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch gptkb:Argo_Workflows XG Boost |
| gptkbp:is_documented_in |
Kubeflow documentation
|
| gptkbp:is_integrated_with |
gptkb:TFX
gptkb:Seldon_Core M Lflow |
| gptkbp:is_open_source |
gptkb:theorem
|
| gptkbp:is_part_of |
cloud-native applications
Kubeflow ecosystem ML Ops tools |
| gptkbp:is_supported_by |
gptkb:Kubeflow_community
|
| gptkbp:is_used_by |
data scientists
ML engineers Dev Ops teams |
| gptkbp:is_used_for |
automating ML workflows
building and deploying machine learning workflows logging pipeline runs monitoring ML pipelines orchestrating ML tasks tracking experiments |
| gptkbp:is_used_in |
research projects
production environments |
| gptkbp:part_of |
gptkb:Kubeflow
|
| gptkbp:provides |
UI for managing pipelines
sample pipelines Python client for Kubeflow Pipelines API SDK for creating components |
| gptkbp:released_in |
gptkb:2018
|
| gptkbp:supports |
data preprocessing
custom components model evaluation component-based architecture model deployment model training multi-step workflows CI/ CD for ML |
| gptkbp:written_in |
gptkb:Library
|