Workflow Orchestration
Automating and sequencing AI lifecycle tasks (data ingestion, training, validation, deployment) to enforce governance policies and ensure consistency.
Definition
The use of workflow engines (e.g., Airflow, Kubeflow) to define DAGs that execute each pipeline stage—including embedded policy checks (impact assessments, security scans), resource‐quota enforcement, and artifact registration—ensuring that every model follows the same, auditable process. Governance requires version-controlled workflows, policy-as-code enforcement at each step, and audit logging of DAG executions.
Real-World Example
An enterprise AI team defines an orchestration DAG: it first runs data-quality tests, then bias audits, triggers model training, executes validation suites, and finally deploys via a canary rollout. Any failed step halts the pipeline and notifies governance stakeholders, ensuring consistent, policy-driven deployments.
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