RRepoGEO

REPOGEO REPORT · LITE

mallahyari/ml-practical-usecases

Default branch main · commit 41ef1e6e · scanned 6/30/2026, 11:43:31 AM

GitHub: 1,282 stars · 224 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
    Clarify README overview to differentiate from courses/tools

    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.
    COPY-PASTE FIX
    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. This is a curated reference database for learning, not an interactive course, a system design interview guide, or a software library.
  • highlicense#2
    Add a standard open-source license file

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a standard open-source license, such as MIT or 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
System Design Interview
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. System Design Interview · recommended 1×
  2. AlgoExpert · recommended 1×
  3. Exponent · recommended 1×
  4. Designing Data-Intensive Applications · recommended 1×
  5. Machine Learning System Design Interview · recommended 1×
  • CATEGORY QUERY
    Where can I find real-world machine learning system design examples from top companies?
    you: not recommended
    AI recommended (in order):
    1. System Design Interview
    2. AlgoExpert
    3. Exponent
    4. Designing Data-Intensive Applications
    5. Machine Learning System Design Interview
    6. The Google File System
    7. MapReduce
    8. Grokking the Machine Learning Interview
    9. Educative.io

    AI recommended 9 alternatives but never named mallahyari/ml-practical-usecases. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    I need practical machine learning use cases to understand system architecture decisions.
    you: not recommended
    AI recommended (in order):
    1. AWS Personalize
    2. Apache Kafka (apache/kafka)
    3. Apache Flink (apache/flink)
    4. Redis (redis/redis)
    5. Kubernetes (kubernetes/kubernetes)
    6. Google Cloud Vertex AI
    7. Databricks Lakehouse Platform
    8. Vespa.ai (vespa-engine/vespa)
    9. Amazon SageMaker
    10. Apache Cassandra (apache/cassandra)
    11. Stripe Radar
    12. Hugging Face Transformers (huggingface/transformers)
    13. PyTorch (pytorch/pytorch)
    14. TensorFlow (tensorflow/tensorflow)
    15. AWS Comprehend
    16. Google Cloud Natural Language API
    17. SpaCy (explosion/spaCy)
    18. Elasticsearch (elastic/elasticsearch)
    19. AWS IoT Core
    20. AWS Kinesis
    21. Azure IoT Hub
    22. Azure Stream Analytics
    23. Azure Machine Learning
    24. InfluxDB (influxdata/influxdb)
    25. Grafana (grafana/grafana)
    26. AWS Rekognition Custom Labels
    27. Google Cloud Vision AI
    28. NVIDIA Jetson
    29. OpenCV (opencv/opencv)
    30. Labelbox
    31. Scale AI

    AI recommended 31 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