RRepoGEO

REPOGEO REPORT · LITE

kubeflow/katib

Default branch master · commit 33c8ff4e · scanned 6/30/2026, 6:16:48 PM

GitHub: 1,687 stars · 528 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
89 /100
Healthy
Category recall
2 / 2
Avg rank #2.0 when recommended
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/katib, 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
  • highreadme#1
    Reposition README opening to emphasize intelligent automation

    Why:

    CURRENT
    Kubeflow Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports Hyperparameter Tuning, Early Stopping and Neural Architecture Search.
    COPY-PASTE FIX
    Kubeflow Katib is the Kubernetes-native solution for **intelligent automated machine learning (AutoML)**, going beyond basic job orchestration to provide advanced Hyperparameter Tuning, Early Stopping, and Neural Architecture Search. It empowers data scientists and ML engineers to efficiently optimize models directly within their Kubernetes clusters.
  • mediumreadme#2
    Add a comparison statement in README for direct competitors

    Why:

    COPY-PASTE FIX
    Add a new paragraph or sentence after the initial description: "Unlike general-purpose AutoML libraries, Katib is purpose-built for Kubernetes, leveraging its native capabilities to orchestrate and manage distributed ML experiments seamlessly within your cluster."
  • lowtopics#3
    Expand topics with more specific ML experimentation terms

    Why:

    CURRENT
    ai, automl, huggingface, hyperparameter-tuning, jax, kubeflow, kubernetes, llm, machine-learning, mlops, neural-architecture-search, pytorch, scikit-learn, tensorflow
    COPY-PASTE FIX
    ai, automl, distributed-ml, huggingface, hyperparameter-tuning, jax, kubeflow, kubernetes, llm, machine-learning, ml-experimentation, mlops, model-optimization, neural-architecture-search, pytorch, scikit-learn, tensorflow

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
2 / 2
100% of queries surface kubeflow/katib
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
Kubeflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Kubeflow · recommended 2×
  2. Ray Tune · recommended 2×
  3. KubeRay · recommended 2×
  4. Kubernetes Jobs · recommended 2×
  5. Optuna · recommended 1×
  • CATEGORY QUERY
    How can I automate hyperparameter tuning for machine learning models deployed on Kubernetes?
    you: #2
    AI recommended (in order):
    1. Kubeflow
    2. Katib ← you
    3. Ray Tune
    4. KubeRay
    5. Optuna
    6. Kubernetes Jobs
    7. Argo Workflows
    8. MLflow
    9. Kubernetes Jobs
    Show full AI answer
  • CATEGORY QUERY
    What tools provide neural architecture search and AutoML capabilities for ML workloads on Kubernetes?
    you: #2
    AI recommended (in order):
    1. Kubeflow
    2. Katib ← you
    3. Google Cloud Vertex AI
    4. Anthos
    5. GKE
    6. Microsoft Azure Machine Learning
    7. Azure Kubernetes Service
    8. AKS
    9. OpenVINO
    10. NNCF
    11. Ray Tune
    12. KubeRay
    13. SigOpt
    14. AutoKeras
    15. AutoGluon
    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/katib?
    pass
    AI named kubeflow/katib 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/katib in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kubeflow/katib 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/katib solve, and who is the primary audience?
    pass
    AI named kubeflow/katib explicitly

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

Embed your GEO score

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kubeflow/katib — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite