RRepoGEO

REPOGEO REPORT · LITE

fmind/mlops-python-package

Default branch main · commit d8715c46 · scanned 5/13/2026, 8:17:22 AM

GitHub: 1,408 stars · 200 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 fmind/mlops-python-package, 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 the README's opening statement to emphasize 'template' nature

    Why:

    CURRENT
    This repository contains a Python code base with best practices designed to support your MLOps initiatives.
    COPY-PASTE FIX
    This repository provides a comprehensive Python package template and starter kit, incorporating best practices to kickstart and standardize your MLOps initiatives and data pipelines.
  • mediumtopics#2
    Add more specific MLOps template-related topics

    Why:

    CURRENT
    automation, data-engineering, data-pipelines, data-science, machine-learning, machine-learning-operations, mlflow, mlops, pandera, pydantic, python, python-template
    COPY-PASTE FIX
    automation, data-engineering, data-pipelines, data-science, machine-learning, machine-learning-operations, mlflow, mlops, mlops-template, pandera, pydantic, python, python-template, starter-kit
  • lowcomparison#3
    Add a dedicated comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example: '## Comparison to Alternatives' that briefly outlines how fmind/mlops-python-package differentiates itself from other MLOps templates (e.g., Cookiecutter Data Science) or broader MLOps frameworks (e.g., Kedro, MLflow) by focusing on providing a ready-to-use, opinionated Python package structure for MLOps.

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 fmind/mlops-python-package
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MLflow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MLflow · recommended 1×
  2. Cookiecutter Data Science · recommended 1×
  3. Kedro · recommended 1×
  4. DVC · recommended 1×
  5. Ploomber · recommended 1×
  • CATEGORY QUERY
    How to standardize machine learning operations development with a Python template?
    you: not recommended
    AI recommended (in order):
    1. MLflow
    2. Cookiecutter Data Science
    3. Kedro
    4. DVC
    5. Ploomber
    6. Metaflow

    AI recommended 6 alternatives but never named fmind/mlops-python-package. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a comprehensive Python package to kickstart MLOps and data science pipelines.
    you: not recommended
    AI recommended (in order):
    1. MLflow (mlflow/mlflow)
    2. Kubeflow Pipelines (kubeflow/pipelines)
    3. Metaflow (Netflix/metaflow)
    4. Apache Airflow (apache/airflow)
    5. Kedro (kedro-org/kedro)
    6. DVC (Data Version Control) (iterative/dvc)

    AI recommended 6 alternatives but never named fmind/mlops-python-package. 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 fmind/mlops-python-package?
    pass
    AI named fmind/mlops-python-package explicitly

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

  • If a team adopts fmind/mlops-python-package in production, what risks or prerequisites should they evaluate first?
    pass
    AI named fmind/mlops-python-package 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 fmind/mlops-python-package solve, and who is the primary audience?
    pass
    AI named fmind/mlops-python-package explicitly

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

Embed your GEO score

Drop this badge into the README of fmind/mlops-python-package. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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