RRepoGEO

REPOGEO REPORT · LITE

kubeflow/community-distribution

Default branch master · commit f09f3eea · scanned 6/13/2026, 12:31:46 PM

GitHub: 1,023 stars · 1,065 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface kubeflow/community-distribution, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Correct typo in existing topics

    Why:

    CURRENT
    deployment, enterpise, kubeflow, multi-tenancy, secure
    COPY-PASTE FIX
    deployment, enterprise, kubeflow, multi-tenancy, secure
  • highabout#2
    Enhance the repository description

    Why:

    CURRENT
    Kubeflow Community Distribution
    COPY-PASTE FIX
    The official community distribution for deploying a secure, multi-tenant Kubeflow MLOps platform on Kubernetes for enterprise and academic users.
  • mediumtopics#3
    Add more specific topics

    Why:

    CURRENT
    deployment, enterprise, kubeflow, multi-tenancy, secure
    COPY-PASTE FIX
    deployment, enterprise, kubeflow, multi-tenancy, secure, mlops, machine-learning-platform, kubernetes-deployment, reference-architecture

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface kubeflow/community-distribution
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Amazon SageMaker
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Amazon SageMaker · recommended 2×
  2. Azure Machine Learning · recommended 2×
  3. Azure Active Directory · recommended 2×
  4. Azure Policy · recommended 2×
  5. Domino Data Lab · recommended 2×
  • CATEGORY QUERY
    How to deploy a secure machine learning platform on Kubernetes for enterprise use?
    you: not recommended
    AI recommended (in order):
    1. OpenShift
    2. Open Data Hub (ODH) (opendatahub-io/opendatahub-operator)
    3. Red Hat OpenShift AI
    4. Kubeflow (kubeflow/kubeflow)
    5. Seldon Core (SeldonIO/seldon-core)
    6. Pachyderm (pachyderm/pachyderm)
    7. Prometheus (prometheus/prometheus)
    8. Grafana (grafana/grafana)
    9. JupyterHub (jupyterhub/jupyterhub)
    10. Google Kubernetes Engine (GKE)
    11. Vertex AI
    12. GKE Sandbox (gVisor) (google/gvisor)
    13. Workload Identity
    14. Binary Authorization
    15. Amazon Elastic Kubernetes Service (EKS)
    16. Amazon SageMaker
    17. Fargate
    18. Azure Kubernetes Service (AKS)
    19. Azure Machine Learning
    20. Azure Active Directory
    21. Azure Policy
    22. Azure Network Security Groups
    23. Vanilla Kubernetes (kubernetes/kubernetes)
    24. kubeadm (kubernetes/kubeadm)
    25. Rancher (rancher/rancher)
    26. HashiCorp Vault (hashicorp/vault)
    27. Kubernetes Secrets Store CSI Driver (kubernetes-sigs/secrets-store-csi-driver)
    28. KServe (kserve/kserve)
    29. Clair (quay/clair)
    30. Trivy (aquasecurity/trivy)
    31. Falco (falcosecurity/falco)
    32. Aqua Security
    33. Sysdig Secure
    34. Domino Data Lab
    35. MLflow (mlflow/mlflow)
    36. Databricks
    37. Active Directory
    38. Okta
    39. Azure Key Vault
    40. AWS Secrets Manager
    41. ELK Stack
    42. Splunk

    AI recommended 42 alternatives but never named kubeflow/community-distribution. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are robust options for multi-tenant machine learning environments across various cloud providers?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes
    2. Kubeflow
    3. Istio
    4. Envoy
    5. AWS EKS
    6. Azure AKS
    7. Google GKE
    8. Databricks
    9. Delta Lake
    10. MLflow
    11. Amazon SageMaker
    12. SageMaker Domains
    13. SageMaker Studio
    14. AWS Organizations
    15. Service Control Policies
    16. IAM
    17. Azure Machine Learning
    18. Azure ML Workspaces
    19. Azure Managed Endpoints
    20. Azure Active Directory
    21. Azure Policy
    22. Google Cloud Vertex AI
    23. Google Cloud Projects
    24. Vertex AI Workbenches
    25. IAM
    26. Domino Data Lab

    AI recommended 26 alternatives but never named kubeflow/community-distribution. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of kubeflow/community-distribution?
    pass
    AI named kubeflow/community-distribution explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts kubeflow/community-distribution in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kubeflow/community-distribution explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo kubeflow/community-distribution solve, and who is the primary audience?
    pass
    AI named kubeflow/community-distribution explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite