Declarative AI Stacks: Managing Complex OpenShift AI Dependencies via GitOps and Helm

Red Hat released an automated framework for deploying Red Hat OpenShift AI dependencies using Helm and GitOps workflows powered by Argo CD. Built around the open-source odh-gitops repository and the rhai-on-openshift-chart, the new implementation packages the multi-step lifecycle of dependent operators—including the NVIDIA GPU Operator, cert-manager, Authorino, and Kueue—into a single declarative deployment. The release supports both complete platform installations and tailored inference-only profiles for model serving, while offering tri-state dependency toggles and integration workarounds for Argo CD sync-time evaluation constraints. AI infrastructure on Kubernetes has historically resisted clean GitOps adoption due to deep operator-level interdependencies and custom resource ordering conflicts. Standard machine learning stacks require coordinating hardware acceleration drivers, cluster admission webhooks, workload schedulers, and mesh security before any model container can initialize. When teams provision these prerequisites using imperative scripts or separate CI jobs, configuration drift and broken cluster upgrades inevitably occur. Standardizing the end-to-end dependency chain into a declarative Helm chart synced by Argo CD enables platform teams to maintain automated, self-healing AI control planes across hybrid cloud environments. This development reflects the broader convergence of platform engineering, AI infrastructure management, and GitOps. As enterprise machine learning operations (MLOps) mature, organizations are moving away from bespoke sandbox clusters toward shared, production-grade Kubernetes platforms. However, managing AI platforms as code presents unique GitOps hurdles, such as Argo CD's inability to access cluster state during client-side Helm template rendering. The new architecture resolves this tension by providing explicit toggles like skipCrdCheck and custom sync parameters, allowing GitOps engines to reconcile complex custom resource definitions without tripping dry-run validation failures. For practitioners, adopting this GitOps pattern requires adjusting standard Argo CD synchronization policies to handle prerequisite Custom Resource Definitions (CRDs) correctly. Teams must enable repository secrets for authentication against protected registries and structure their environment overlays to pin operator versions across development and production stages. Furthermore, platform engineers deploying AI inference at the edge or in resource-constrained environments can leverage profile-based values files to strip out heavy training components, minimizing cluster overhead while preserving declarative drift detection and rapid rollback capabilities.