slug: zenml
provider: ZenML
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: 4
edges:
- tag: Models
  spec_file: zenml-models-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.85
  evidence: '''List Models'', ''Create Model'', ''List Model Versions''; schemas ''ModelVersion'', ''ModelRequest'''
  reason: Registration and versioning of ML models in an open-source MLOps framework is squarely AI/ML
    model lifecycle (MLOps) management.
- tag: Artifacts
  spec_file: zenml-artifacts-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.75
  evidence: '''List Artifacts'' / schemas ''Artifact'', ''ArtifactPage'' in the ''REST API for the ZenML
    server'' managing ''pipelines, stacks, components, artifacts, models'''
  reason: Versioned ML pipeline artifacts in an MLOps framework sit within AI/ML model lifecycle and MLOps
    management; read-only listing keeps confidence moderate.
- tag: Pipeline Runs
  spec_file: zenml-pipeline-runs-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.75
  evidence: '''List Pipeline Runs'', ''Get Pipeline Run''; schemas ''PipelineRun'', ''RunStatus'''
  reason: Tracking executions of ML pipelines on the ZenML server is MLOps execution/lineage management,
    part of AI/ML model lifecycle rather than generic IT operations.
- tag: Pipelines
  spec_file: zenml-pipelines-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.75
  evidence: POST /pipelines createPipeline Create Pipeline; "ZenML is an open-source MLOps and LLMOps
    framework ... managing pipelines, stacks, artifacts, models, and deployments"
  reason: The operations create and manage ML/GenAI pipeline definitions on an MLOps server, which is
    the AI/ML model lifecycle (MLOps) capability rather than generic workflow or data engineering.