II.
Workflow overview
Reference · liveworkflow:model-deployment-pipeline
Model Deployment Pipeline overview
Deploys trained ML models to production serving infrastructure — model packaging, canary rollout, latency and accuracy validation, A/B traffic splitting, and rollback procedures. Excludes model training.
Attributes
displayName
Model Deployment Pipeline
workflowKind
release
triggerType
event-driven
typicalCadence
per-model
complexity
cross-team
description
Deploys trained ML models to production serving infrastructure — model
packaging, canary rollout, latency and accuracy validation, A/B traffic
splitting, and rollback procedures. Excludes model training.
Outgoing edges
applies_to_domain2
- domain:ml-ops·DomainMLOps
- domain:platform-engineering·DomainPlatform Engineering
involves_role2
- role:ml-engineer·RoleMachine Learning Engineer
- 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:canary-rollouts·SkillAreaCanary Rollouts
- skill-area:ml-fine-tuning·SkillAreaML Fine-Tuning
- skill-area:observability-pipeline·SkillAreaObservability Pipeline
triggers_responsibility2
- responsibility:approve-deploys·ResponsibilityApprove production deploys
- responsibility:slo-definition·ResponsibilitySLO definition
Incoming edges
follows_workflow2
- stack-profile:feature-store-mlops·StackProfileFeature Store & MLOps Stack (Feast, MLflow, BentoML, K8s, Prometheus)
- stack-profile:edge-ai-iot·StackProfileEdge AI / IoT Stack (TensorFlow Lite, MQTT, Rust, InfluxDB, Grafana)