II.
Workflow overview
Reference · liveworkflow:model-training-cycle
Model Training Cycle overview
Manages the end-to-end ML model training lifecycle — dataset preparation, hyperparameter sweeps, distributed training orchestration, experiment tracking, and model artifact registration. Excludes model serving.
Attributes
displayName
Model Training Cycle
workflowKind
data
triggerType
event-driven
typicalCadence
per-experiment
complexity
cross-team
description
Manages the end-to-end ML model training lifecycle — dataset preparation,
hyperparameter sweeps, distributed training orchestration, experiment
tracking, and model artifact registration. Excludes model serving.
Outgoing edges
applies_to_domain2
- domain:data-science·DomainData Science
- domain:ml-ops·DomainMLOps
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:ml-platform-team·OrgUnitML Platform 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
follows_workflow2
- stack-profile:llm-fine-tuning·StackProfileLLM Fine-Tuning Stack (PyTorch, HuggingFace, PEFT/LoRA, W&B, vLLM)
- stack-profile:synthetic-data-generation·StackProfileSynthetic Data Generation Stack (Python, PyTorch, FastAPI, PostgreSQL, S3)