RRepoGEO

REPOGEO REPORT · LITE

mallahyari/ml-practical-usecases

Default branch main · commit 41ef1e6e · scanned 5/19/2026, 4:17:28 AM

GitHub: 1,269 stars · 225 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
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 mallahyari/ml-practical-usecases, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Emphasize the repo's role as a curated database in the README overview

    Why:

    CURRENT
    This repository contains a database of **650 case studies** from over **100 companies**, showcasing how companies like Netflix, Airbnb, and Doordash apply machine learning to enhance their products and processes. These case studies provide practical ML use cases and valuable learnings from designing ML systems.
    COPY-PASTE FIX
    This repository serves as a **curated, centralized database** of **650 case studies** from over **100 companies**, showcasing how companies like Netflix, Airbnb, and Doordash apply machine learning to enhance their products and processes. Unlike scattered blogs or company tech sites, this collection provides practical ML use cases and valuable learnings from designing ML systems in one accessible place.
  • mediumlicense#2
    Add a standard open-source license file

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the text of the MIT License (or another suitable open-source license like Apache-2.0).

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 mallahyari/ml-practical-usecases
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Netflix TechBlog
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Netflix TechBlog · recommended 1×
  2. Google AI Blog · recommended 1×
  3. Meta AI · recommended 1×
  4. Uber Engineering Blog · recommended 1×
  5. Amazon Science / AWS Machine Learning Blog · recommended 1×
  • CATEGORY QUERY
    Where can I find real-world machine learning system design examples from major companies?
    you: not recommended
    AI recommended (in order):
    1. Netflix TechBlog
    2. Google AI Blog
    3. Meta AI
    4. Uber Engineering Blog
    5. Amazon Science / AWS Machine Learning Blog
    6. NeurIPS
    7. KDD
    8. RecSys
    9. MLOps World
    10. Data Council
    11. Google Scholar
    12. arXiv
    13. "Designing Data-Intensive Applications" by Martin Kleppmann
    14. Grokking the Machine Learning Interview (Educative.io)
    15. "Machine Learning System Design" by Alex Xu (ByteByteGo)

    AI recommended 15 alternatives but never named mallahyari/ml-practical-usecases. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    I need practical examples of how companies implement machine learning in their products.
    you: not recommended
    AI recommended (in order):
    1. Amazon
    2. Netflix
    3. Spotify
    4. Google Search
    5. RankBrain
    6. BERT
    7. Gmail
    8. Grammarly
    9. Apple Photos
    10. Google Photos
    11. Tesla
    12. Pinterest
    13. PayPal
    14. FICO
    15. General Electric
    16. Predix

    AI recommended 16 alternatives but never named mallahyari/ml-practical-usecases. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 mallahyari/ml-practical-usecases?
    pass
    AI did not name mallahyari/ml-practical-usecases — 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 mallahyari/ml-practical-usecases in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mallahyari/ml-practical-usecases 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 mallahyari/ml-practical-usecases solve, and who is the primary audience?
    pass
    AI did not name mallahyari/ml-practical-usecases — 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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mallahyari/ml-practical-usecases — 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