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Production AI Radar

How to GitOps ML workloads with Flux

Flux-based continuous delivery for model services — image automation, progressive delivery, and audit-friendly Git history.

TrialPlatform & DevEx12 min
Registry → canary → rollback

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Promote only after eval. Canary a traffic slice on K8s; auto-rollback on SLO breach; GitOps is the audit trail.

When you need this

  • Prefer Flux over Argo in an existing GitOps estate
  • Want image update automation when registry promotes
  • Need multi-tenant clusters with Flux Kustomizations
  • Progressive delivery with Flagger + Flux

Prerequisites

  • Flux installed (or plan to bootstrap)
  • Git repo for cluster desired state
  • Container registry webhooks / image policy

Tools

  • Assess as Argo CD peer; pick one GitOps engine and standardize.

  • Start with registry + experiment tracking before full deployment automation.

Steps

  1. 1

    Bootstrap Flux for the AI namespace

    GitRepository + Kustomization for staging/prod overlays. Keep secrets out of Git (SOPS / External Secrets).

  2. 2

    Image automation from registry

    ImageRepository + ImagePolicy watch the model service repo. On MLflow-linked build, Flux opens/updates the image tag in Git.

  3. 3

    Progressive delivery

    Optional Flagger canaries on the Flux-managed Deployment. Promote on metrics; rollback on SLO breach.

  4. 4

    Operate and drill

    Suspend/resume for incidents. Quarterly rollback by Git revert. Document who can reconcile prod.

Adoption pitfalls

  • Image automation writing to prod without PR in locked-down setups
  • Drift from kubectl edit
  • No link between Flux commit and MLflow version

Adoption checklist

  • Desired state only in Git
  • Image tags updated by automation, not humans in cluster
  • Prod change requires PR
  • Rollback drill completed

SEER REAL assessment / sprint

Assessment maps GitOps tool choice and gaps. Sprint bootstraps Flux for one model service with image automation and a rollback drill.

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