RRepoGEO

REPOGEO REPORT · LITE

kelvins/awesome-mlops

Default branch main · commit 2fb31352 · scanned 5/26/2026, 5:37:26 PM

GitHub: 5,154 stars · 733 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 kelvins/awesome-mlops, 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 clarify its utility for tool discovery

    Why:

    CURRENT
    A curated list of awesome MLOps tools.
    COPY-PASTE FIX
    A curated list of awesome MLOps tools. Use this list to discover, evaluate, and select the best MLOps tools and resources for your needs.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., a project website, a related blog post, or the GitHub repo URL itself) to the 'Homepage' field in the repository settings.

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 kelvins/awesome-mlops
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
mlflow/mlflow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. mlflow/mlflow · recommended 1×
  2. Weights & Biases (W&B) · recommended 1×
  3. Comet ML · recommended 1×
  4. iterative/dvc · recommended 1×
  5. treeverse/lakefs · recommended 1×
  • CATEGORY QUERY
    What are the essential tools for setting up a robust MLOps pipeline?
    you: not recommended
    AI recommended (in order):
    1. MLflow (mlflow/mlflow)
    2. Weights & Biases (W&B)
    3. Comet ML
    4. DVC (Data Version Control) (iterative/dvc)
    5. LakeFS (treeverse/lakefs)
    6. Pachyderm (pachyderm/pachyderm)
    7. Kubeflow Pipelines (kubeflow/pipelines)
    8. Apache Airflow (apache/airflow)
    9. Argo Workflows (argoproj/argo-workflows)
    10. Seldon Core (SeldonIO/seldon-core)
    11. TensorFlow Serving (tensorflow/serving)
    12. TorchServe (pytorch/serve)
    13. FastAPI (tiangolo/fastapi)
    14. Prometheus (prometheus/prometheus)
    15. Grafana (grafana/grafana)
    16. Evidently AI (evidentlyai/evidently)
    17. Arize AI
    18. Feast (feast-dev/feast)
    19. Tecton

    AI recommended 19 alternatives but never named kelvins/awesome-mlops. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking recommendations for managing machine learning model lifecycle and operational challenges.
    you: not recommended
    AI recommended (in order):
    1. MLflow
    2. Kubeflow
    3. Databricks Lakehouse Platform
    4. Amazon SageMaker
    5. Google Cloud Vertex AI
    6. Azure Machine Learning
    7. Weights & Biases

    AI recommended 7 alternatives but never named kelvins/awesome-mlops. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 kelvins/awesome-mlops?
    pass
    AI did not name kelvins/awesome-mlops — likely talking about a different project

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

  • If a team adopts kelvins/awesome-mlops in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kelvins/awesome-mlops 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 kelvins/awesome-mlops solve, and who is the primary audience?
    pass
    AI did not name kelvins/awesome-mlops — likely talking about a different project

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

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kelvins/awesome-mlops — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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