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Agentic AI Atlas · Model Deployment Pipeline
workflow:model-deployment-pipelinea5c.ai
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workflow:model-deployment-pipeline

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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.

WorkflowOutgoing · 11Incoming · 2

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)

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