II.
Workflow overview
Reference · liveworkflow:hyperparameter-tuning-cycle
Hyperparameter Tuning Cycle overview
Orchestrates systematic hyperparameter optimization — defining search spaces, launching sweep jobs (grid, random, Bayesian), tracking trial results, applying early stopping, and selecting the best configuration for promotion. Excludes model architecture search.
Attributes
displayName
Hyperparameter Tuning Cycle
workflowKind
data
triggerType
event-driven
typicalCadence
per-experiment
complexity
single-team
description
Orchestrates systematic hyperparameter optimization — defining search
spaces, launching sweep jobs (grid, random, Bayesian), tracking trial
results, applying early stopping, and selecting the best configuration
for promotion. Excludes model architecture search.
Outgoing edges
applies_to_domain2
- domain:ml-ops·DomainMLOps
- domain:data-science·DomainData Science
involves_role2
- role:ml-engineer·RoleMachine Learning Engineer
- role:data-scientist·RoleData Scientist
performed_by_org_unit2
- org-unit:ml-team·OrgUnitML Team
- org-unit:research-engineering·OrgUnitResearch Engineering
requires_skill_area2
- skill-area:ml-fine-tuning·SkillAreaML Fine-Tuning
- skill-area:python-data-pipelines·SkillAreaPython Data Pipelines
triggers_responsibility1
- responsibility:data-quality-monitoring·ResponsibilityData quality monitoring
Incoming edges
follows_workflow1
- stack-profile:llm-fine-tuning·StackProfileLLM Fine-Tuning Stack (PyTorch, HuggingFace, PEFT/LoRA, W&B, vLLM)