| --- |
| language: |
| - en |
| license: apache-2.0 |
| size_categories: |
| - 100K<n<1M |
| task_categories: |
| - text-generation |
| - question-answering |
| tags: |
| - devops |
| - kubernetes |
| - infrastructure |
| - sft |
| - synthetic |
| - ci-cd |
| - helm |
| - terraform |
| - gitops |
| - argocd |
| pretty_name: DevOps and Kubernetes SFT 100K |
| --- |
| |
| # DevOps and Kubernetes SFT 100K |
|
|
| A synthetic supervised fine-tuning dataset of 100,000 high-quality DevOps and Kubernetes conversations designed to train AI assistants capable of supporting platform engineers, SREs, and DevOps practitioners. |
|
|
| ## Dataset Description |
|
|
| This dataset covers production-grade Kubernetes operations, cloud infrastructure, CI/CD pipelines, GitOps workflows, and platform engineering across 13 specialized categories. Each record follows the ShareGPT conversation format with practical, copy-paste-ready examples. |
|
|
| ## Categories |
|
|
| | Category | Description | |
| |---|---| |
| | `kubernetes_troubleshooting` | Pod debugging, CrashLoopBackOff, pending pods | |
| | `kubernetes_networking` | Ingress, Services, NetworkPolicies, DNS | |
| | `kubernetes_security` | RBAC, PodSecurity, NetworkPolicy, secrets management | |
| | `kubernetes_storage` | PVCs, StatefulSets, StorageClasses, volume snapshots | |
| | `kubernetes_autoscaling` | HPA, VPA, KEDA, Cluster Autoscaler | |
| | `kubernetes_debugging` | Ephemeral containers, network debugging, pprof | |
| | `kubernetes_cost_optimization` | Right-sizing, Spot instances, waste identification | |
| | `ci_cd_pipelines` | GitHub Actions, Docker build, deployment pipelines | |
| | `infrastructure_as_code` | Terraform, EKS, VPC, IRSA | |
| | `helm_charts` | Helm chart creation, lifecycle hooks, best practices | |
| | `gitops_argocd` | ArgoCD, multi-environment promotion, App of Apps | |
| | `monitoring_observability` | Prometheus, Grafana, SLO alerting, kube-prometheus-stack | |
| | `devops_platform_engineering` | IDP, Backstage, Crossplane, ephemeral environments | |
|
|
| ## Format |
|
|
| ShareGPT format: |
| ```json |
| { |
| "conversations": [ |
| {"from": "human", "value": "...DevOps question..."}, |
| {"from": "gpt", "value": "...structured answer with code examples..."} |
| ], |
| "metadata": {"category": "...", "context": "..."}, |
| "id": "uuid" |
| } |
| ``` |
|
|
| ## Use Cases |
|
|
| - Fine-tuning AI coding assistants for platform engineering |
| - SRE and DevOps chatbot development |
| - Kubernetes operational runbook automation |
| - Internal developer platform AI agents |
| - DevOps training and certification preparation |
|
|
| ## Quality Notes |
|
|
| All responses include working code examples, production-ready configurations, and best practice guidance. YAML, shell commands, and Terraform examples are copy-paste ready. |
|
|
