II.
Workflow overview
Reference · liveworkflow:ml-experiment-tracking
ML Experiment Tracking overview
Maintains the experiment tracking infrastructure — enforcing metadata schemas, managing run lineage, pruning abandoned experiments, comparing runs across dimensions, and ensuring reproducibility artifacts are stored alongside results. Excludes experiment design.
Attributes
displayName
ML Experiment Tracking
workflowKind
data
triggerType
continuous
typicalCadence
continuous
complexity
single-team
description
Maintains the experiment tracking infrastructure — enforcing metadata
schemas, managing run lineage, pruning abandoned experiments, comparing
runs across dimensions, and ensuring reproducibility artifacts are stored
alongside results. Excludes experiment design.
Outgoing edges
applies_to_domain2
- domain:ml-ops·DomainMLOps
- domain:data-science·DomainData Science
involves_role3
- role:ml-engineer·RoleMachine Learning Engineer
- role:data-scientist·RoleData Scientist
- role:platform-engineer·RolePlatform Engineer
performed_by_org_unit2
- org-unit:ml-platform-team·OrgUnitML Platform Team
- org-unit:ml-team·OrgUnitML Team
requires_skill_area3
- skill-area:ml-fine-tuning·SkillAreaML Fine-Tuning
- skill-area:python-data-pipelines·SkillAreaPython Data Pipelines
- skill-area:observability-pipeline·SkillAreaObservability Pipeline
triggers_responsibility1
- responsibility:data-quality-monitoring·ResponsibilityData quality monitoring
Incoming edges
None.