# ML Model Deployment

Published articles for ML Model Deployment.

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## Using ArgoCD to Manage AI Model Deployments with GitOps

DevFeed: [Using ArgoCD to Manage AI Model Deployments with GitOps](<https://devfeed.tech/articles/using-argocd-to-manage-ai-model-deployments-with-gitops-17479.md>)

Original publisher: [Read original article](<https://kodekloud.com/blog/argocd-gitops-ai-model-deployments/>)

Author: Pramodh Kumar M

Published: 2026-07-28T18:24:57Z

Content type: tutorial

Language: en

Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More](<https://devfeed.tech/sources/kubernetes-kodekloud-blog-devops-cloud-kubernetes-ai-tutorials-more.md>)

Topics: [GitOps](<https://devfeed.tech/topics/gitops.md>), [argocd](<https://devfeed.tech/topics/argocd.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [model-serving](<https://devfeed.tech/topics/model-serving.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [argo-rollouts-model-promotion](<https://devfeed.tech/tags/argo-rollouts-model-promotion.md>), [argocd](<https://devfeed.tech/tags/argocd.md>), [argocd-ai-model-deployment](<https://devfeed.tech/tags/argocd-ai-model-deployment.md>), [argocd-applicationset](<https://devfeed.tech/tags/argocd-applicationset.md>), [argocd-sync-waves](<https://devfeed.tech/tags/argocd-sync-waves.md>), [automation](<https://devfeed.tech/tags/automation.md>), [ci-cd](<https://devfeed.tech/tags/ci-cd.md>), [declarative-model-serving](<https://devfeed.tech/tags/declarative-model-serving.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [devops](<https://devfeed.tech/tags/devops.md>), [gitops](<https://devfeed.tech/tags/gitops.md>), [gitops-for-ai-model-deployments](<https://devfeed.tech/tags/gitops-for-ai-model-deployments.md>), [gitops-mlops](<https://devfeed.tech/tags/gitops-mlops.md>), [kserve](<https://devfeed.tech/tags/kserve.md>), [kserve-canary-deployment](<https://devfeed.tech/tags/kserve-canary-deployment.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [kubernetes-model-deployment-rollback](<https://devfeed.tech/tags/kubernetes-model-deployment-rollback.md>), [ml-model-deployment](<https://devfeed.tech/tags/ml-model-deployment.md>), [ml-model-versioning](<https://devfeed.tech/tags/ml-model-versioning.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [model-deployment](<https://devfeed.tech/tags/model-deployment.md>), [model-registry-to-git-automation](<https://devfeed.tech/tags/model-registry-to-git-automation.md>), [model-serving](<https://devfeed.tech/tags/model-serving.md>), [pull-requests](<https://devfeed.tech/tags/pull-requests.md>), [standard](<https://devfeed.tech/tags/standard.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

### AI overview

This tutorial explains how to use ArgoCD and GitOps to deploy AI models while accounting for model weights stored outside container images. It covers pinned model references, rollback and recovery, synchronization ordering, readiness probes, evaluation-based promotion gates, and KServe status handling.

### Source excerpt

Your microservices deploy through pull requests with full audit trails. Your models deploy because someone ran a script. Here is how to close that gap with ArgoCD, and what changes when the artifact weighs four gigabytes.