slug: kubeflow
provider: Kubeflow
generated_by: planning/capability-mapping/scripts/classify_capabilities.py
model: claude-opus-5
frame:
- Software & Technology
min_confidence: 0.7
capability_model:
  source: https://github.com/vincentmakes/turbo-ea-capabilities
  license: CC-BY-4.0
  attribution: Turbo EA Capabilities by Vincent Verdet — Turbo EA, https://github.com/vincentmakes/turbo-ea-capabilities,
    CC BY 4.0
  notice: NOTICE
edge_count: 3
edges:
- tag: Pipelines
  spec_file: kubeflow-pipelines-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.8
  evidence: POST /apis/v2beta1/pipelines  createPipeline  Create a pipeline — 'open-source machine learning
    platform ... tools for training, serving, tuning, and managing ML models'
  reason: Management of machine-learning pipeline definitions; squarely AI/ML model lifecycle and MLOps.
- tag: Experiments
  spec_file: kubeflow-experiments-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.75
  evidence: POST /apis/v2beta1/experiments  createExperiment  Create a new experiment; schema Experiment;
    'machine learning workflows running on Kubernetes'
  reason: ML experiment grouping for pipeline runs — MLOps construct, not product A/B experimentation
    (BC-4280 would be the homograph trap here).
- tag: Pipeline Versions
  spec_file: kubeflow-pipeline-versions-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.75
  evidence: GET /apis/v2beta1/pipelines/{pipeline_id}/versions  listPipelineVersions  List versions of
    a pipeline
  reason: Version lifecycle of ML workflow definitions on an ML platform — MLOps / AI lifecycle management.