RRepoGEO

REPOGEO REPORT · LITE

GokuMohandas/mlops-course

Default branch main · commit de51e659 · scanned 6/20/2026, 12:43:03 PM

GitHub: 3,382 stars · 599 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
27 /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
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 GokuMohandas/mlops-course, 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 H1 and opening paragraph to emphasize 'curriculum'

    Why:

    CURRENT
    # MLOps Course

Learn how to combine machine learning with software engineering to design, develop, deploy and iterate on production-grade ML applications.
    COPY-PASTE FIX
    # MLOps Course: The Full Curriculum

This repository contains the complete curriculum for the MLOps Course, designed to teach you how to combine machine learning with software engineering to design, develop, deploy and iterate on production-grade ML applications.
  • mediumtopics#2
    Add explicit educational topics to reinforce 'course' identity

    Why:

    CURRENT
    data-engineering, data-quality, data-science, deep-learning, distributed-ml, llms, machine-learning, mlops, natural-language-processing, python, pytorch, ray
    COPY-PASTE FIX
    mlops, machine-learning, python, pytorch, ray, data-science, deep-learning, llms, natural-language-processing, data-engineering, data-quality, distributed-ml, course, education, curriculum, tutorial
  • lowabout#3
    Refine the repository description to explicitly mention 'course'

    Why:

    CURRENT
    Learn how to design, develop, deploy and iterate on production-grade ML applications.
    COPY-PASTE FIX
    A comprehensive MLOps course to learn how to design, develop, deploy and iterate on production-grade ML applications.

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 GokuMohandas/mlops-course
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. kubeflow/kubeflow · recommended 1×
  4. mlflow/mlflow · recommended 1×
  5. Dataiku DSS · recommended 1×
  • CATEGORY QUERY
    How can I learn to build and deploy production-grade machine learning applications reliably?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow (kubeflow/kubeflow)
    2. MLflow (mlflow/mlflow)
    3. Dataiku DSS
    4. Amazon SageMaker
    5. Vertex AI
    6. Azure Machine Learning
    7. DVC (iterative/dvc)

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

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices for scaling ML workloads using Python and modern frameworks?
    you: not recommended
    AI recommended (in order):
    1. Dask
    2. Ray
    3. PyTorch Distributed
    4. TensorFlow Distributed
    5. Apache Airflow
    6. Prefect
    7. Feast
    8. Hopsworks
    9. FastAPI
    10. Kubernetes
    11. MLflow
    12. Google Cloud Vertex AI
    13. Amazon SageMaker
    14. Azure Machine Learning

    AI recommended 14 alternatives but never named GokuMohandas/mlops-course. 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 GokuMohandas/mlops-course?
    pass
    AI did not name GokuMohandas/mlops-course — 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 GokuMohandas/mlops-course in production, what risks or prerequisites should they evaluate first?
    pass
    AI named GokuMohandas/mlops-course 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 GokuMohandas/mlops-course solve, and who is the primary audience?
    pass
    AI did not name GokuMohandas/mlops-course — 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?

Embed your GEO score

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

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/GokuMohandas/mlops-course.svg)](https://repogeo.com/en/r/GokuMohandas/mlops-course)
HTML
<a href="https://repogeo.com/en/r/GokuMohandas/mlops-course"><img src="https://repogeo.com/badge/GokuMohandas/mlops-course.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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

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