GitOps ML Best Practices Advanced
This final lesson distills the course into actionable best practices for running GitOps-managed ML infrastructure in production. These patterns are drawn from organizations successfully managing hundreds of ML models across multiple clusters.
Repository Strategy
- Separate infrastructure and application repos — Keep platform-level infrastructure (controllers, operators, networking) in a dedicated repo, and ML application configs in team-owned repos
- Use monorepo for related ML services — Group related models and pipelines in a single repo for atomic cross-service changes
- Branch protection — Require PR reviews for production changes; use CODEOWNERS to assign ML platform team as reviewers for infrastructure changes
- Semantic versioning for manifests — Tag releases of infrastructure configs to enable rollback to known-good states
Secrets Management
Never store plain-text secrets in Git. Use one of these approaches:
| Approach | How It Works | Best For |
|---|---|---|
| Sealed Secrets | Encrypt secrets with a cluster-specific key; only the controller can decrypt | Simple setups, single cluster |
| SOPS + Age/KMS | Encrypt secret values in YAML files using Mozilla SOPS with cloud KMS or age keys | Multi-cloud, Flux-native integration |
| External Secrets Operator | Sync secrets from Vault, AWS Secrets Manager, or GCP Secret Manager into Kubernetes | Enterprise, existing secret stores |
Multi-Environment Promotion
- Dev: Auto-sync from feature branches
Developers push to feature branches; ArgoCD/Flux auto-deploys to dev clusters for rapid iteration.
- Staging: PR-based promotion
Merge to staging branch triggers deployment; run integration tests and model validation before production.
- Production: Controlled rollout
PR from staging to main with required approvals; use canary deployments for model serving changes.
Disaster Recovery
- Git is your backup — Since all infrastructure state is in Git, you can recreate an entire cluster by pointing ArgoCD/Flux at the repository
- Bootstrap scripts — Maintain tested bootstrap scripts that install the GitOps controller and connect it to your repos
- Regular DR drills — Practice restoring ML infrastructure from Git to verify your recovery process works within SLA
- Persistent data — GitOps manages compute, not data; ensure model artifacts and datasets have separate backup strategies
Course Complete: You now understand how to implement GitOps for ML infrastructure using ArgoCD and Flux, design ML-specific Kubernetes manifests, detect and remediate drift, and follow production best practices. Apply these patterns to build reliable, auditable, and reproducible ML platforms.
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