Orchestration
The automated coordination of AI workflows and services - data ingestion, model training, deployment - ensuring compliance with policies and resource governance.
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.
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.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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What problem does Enzai solve?
Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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