REPOGEO REPORT · LITE

fmind/mlops-python-package

Default branch main · commit d8715c46 · scanned 6/23/2026, 7:12:13 PM

GitHub: 1,413 stars · 200 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

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"

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 opinionated codebase, designed with best practices to kickstart and standardize your MLOps initiatives.**

mediumabout#2

Refine the "About" description to highlight "template" and "starter kit"

Why:

CURRENT

A comprehensive Python package template to kickstart and standardize your MLOps initiatives and data pipelines.

COPY-PASTE FIX

An opinionated Python package template and starter kit for MLOps, providing a standardized codebase to accelerate your machine learning operations and data pipeline development.

lowtopics#3

Add `mlops-template` to the repository 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

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

Of all named tools, what % are you?

Top rival

kubeflow/pipelines

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

kubeflow/pipelines · recommended 1×
mlflow/mlflow · recommended 1×
apache/airflow · recommended 1×
Netflix/metaflow · recommended 1×
PrefectHQ/prefect · recommended 1×

CATEGORY QUERY
How can I standardize my machine learning operations and data pipelines using Python?
you: not recommended
AI recommended (in order):
1. Kubeflow Pipelines (kubeflow/pipelines)
2. MLflow (mlflow/mlflow)
3. Apache Airflow (apache/airflow)
4. Metaflow (Netflix/metaflow)
5. Prefect (PrefectHQ/prefect)
6. DVC (iterative/dvc)
7. Kedro (kedro-org/kedro)
AI recommended 7 alternatives but never named fmind/mlops-python-package. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a robust Python template to kickstart MLOps projects with experiment tracking and data validation.
you: not recommended
AI recommended (in order):
1. MLflow
2. Great Expectations
3. Pydantic
4. Kedro
5. Weights & Biases
6. DVC
7. CML
8. Pandera
9. Cookiecutter Data Science
10. Comet ML
11. Ploomber
AI recommended 11 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

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/fmind/mlops-python-package"><img src="https://repogeo.com/badge/fmind/mlops-python-package.svg" alt="RepoGEO" /></a>

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