RRepoGEO

REPOGEO REPORT · LITE

zhaochenyang20/Awesome-ML-SYS-Tutorial

Default branch main · commit 642400ec · scanned 6/26/2026, 11:02:46 PM

GitHub: 6,589 stars · 451 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 zhaochenyang20/Awesome-ML-SYS-Tutorial, 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
    Reposition the README's opening to clarify its nature as a curated resource collection

    Why:

    CURRENT
    My learning notes for ML SYS.
    COPY-PASTE FIX
    This repository is a curated collection of learning notes, tutorials, papers, and resources for Machine Learning Systems (ML-SYS), focusing on foundational concepts and common challenges in infrastructure.
  • mediumhomepage#2
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/zhaochenyang20/Awesome-ML-SYS-Tutorial

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 zhaochenyang20/Awesome-ML-SYS-Tutorial
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 3×
  2. Google Cloud · recommended 1×
  3. Vertex AI · recommended 1×
  4. Dataflow · recommended 1×
  5. BigQuery · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive tutorials for understanding machine learning system infrastructure?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud
    2. Vertex AI
    3. Dataflow
    4. BigQuery
    5. AWS
    6. Amazon SageMaker
    7. AWS Lambda
    8. Amazon S3
    9. Amazon Kinesis
    10. Microsoft Azure
    11. Azure Machine Learning service
    12. Azure Databricks
    13. Azure Kubernetes Service (AKS)
    14. Azure Data Factory
    15. Coursera
    16. DeepLearning.AI
    17. Databricks Academy
    18. Apache Spark
    19. Delta Lake
    20. MLflow
    21. Kubeflow

    AI recommended 21 alternatives but never named zhaochenyang20/Awesome-ML-SYS-Tutorial. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Resources for deep diving into common challenges and flaws in reinforcement learning infrastructure?
    you: not recommended
    AI recommended (in order):
    1. Ray RLlib (ray-project/ray)
    2. Tune (ray-project/ray)
    3. Ray (ray-project/ray)
    4. Stable Baselines3 (DLR-RM/stable-baselines3)
    5. Gymnasium (Farama-Foundation/Gymnasium)
    6. OpenAI Gym (openai/gym)
    7. TensorBoard (tensorflow/tensorboard)
    8. Acme (deepmind/acme)
    9. reverb (deepmind/reverb)
    10. OpenSpiel (deepmind/open_spiel)
    11. Reinforcement Learning: An Introduction
    12. Deep Reinforcement Learning Hands-On

    AI recommended 12 alternatives but never named zhaochenyang20/Awesome-ML-SYS-Tutorial. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 zhaochenyang20/Awesome-ML-SYS-Tutorial?
    pass
    AI did not name zhaochenyang20/Awesome-ML-SYS-Tutorial — 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 zhaochenyang20/Awesome-ML-SYS-Tutorial in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zhaochenyang20/Awesome-ML-SYS-Tutorial 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 zhaochenyang20/Awesome-ML-SYS-Tutorial solve, and who is the primary audience?
    pass
    AI did not name zhaochenyang20/Awesome-ML-SYS-Tutorial — 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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zhaochenyang20/Awesome-ML-SYS-Tutorial — 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