Orchestration
The automated coordination of AI workflows and services—data ingestion, model training, deployment—ensuring compliance with policies and resource governance.
Definition
Uses workflow engines (Airflow, Kubeflow Pipelines) or container orchestrators (Kubernetes) to sequence tasks: ingesting data, preprocessing, training, validation, and rollout. Orchestration frameworks enforce policy checks (impact assessments, security scans) at each stage, manage resource quotas, and provide retry logic. Governance requires version-controlling workflows, embedding compliance gates, and auditing orchestration logs to prove policy adherence.
Real-World Example
A healthcare AI team defines its MLOps pipeline in Kubeflow: it runs data-quality checks, bias assessments, and security scans before training. Upon passing, the model is automatically deployed to staging. All steps and artifacts are recorded in Artifactory, ensuring an auditable, policy-driven orchestration of the entire lifecycle.
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