RRepoGEO

REPOGEO REPORT · LITE

GoogleCloudPlatform/ml-design-patterns

Default branch master · commit 060eb9f9 · scanned 5/26/2026, 5:02:44 AM

GitHub: 2,090 stars · 590 forks

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 GoogleCloudPlatform/ml-design-patterns, 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 clearly state the repo's purpose

    Why:

    CURRENT
    # ml-design-patterns
Source code accompanying O'Reilly book: <br/>
**Title**: Machine Learning Design Patterns <br/>
**Authors**: Valliappa (Lak) Lakshmanan, Sara Robinson, Michael Munn <br/>
    COPY-PASTE FIX
    # Machine Learning Design Patterns: Source Code and Implementations
This repository provides practical source code examples and implementations for the Machine Learning Design Patterns described in the O'Reilly book: **Machine Learning Design Patterns**. It serves as a hands-on resource for ML engineers and architects to understand and apply robust, scalable ML system designs.

**Title**: Machine Learning Design Patterns <br/>
**Authors**: Valliappa (Lak) Lakshmanan, Sara Robinson, Michael Munn <br/>
  • mediumhomepage#2
    Add the O'Reilly book URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://www.oreilly.com/library/view/machine-learning-design/9781098115777/

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 GoogleCloudPlatform/ml-design-patterns
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Apache Kafka
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Apache Kafka · recommended 1×
  2. Amazon Kinesis · recommended 1×
  3. Apache Spark · recommended 1×
  4. Apache Flink · recommended 1×
  5. Apache Kafka Streams · recommended 1×
  • CATEGORY QUERY
    What are common architectural patterns for building robust and scalable machine learning systems?
    you: not recommended
    AI recommended (in order):
    1. Apache Kafka
    2. Amazon Kinesis
    3. Apache Spark
    4. Apache Flink
    5. Apache Kafka Streams
    6. Amazon Kinesis Data Analytics
    7. Hopsworks
    8. Tecton

    AI recommended 8 alternatives but never named GoogleCloudPlatform/ml-design-patterns. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Show me practical code examples for implementing various machine learning design patterns.
    you: not recommended
    AI recommended (in order):
    1. Scikit-learn
    2. TensorFlow
    3. PyTorch
    4. Keras
    5. MLflow
    6. Kubeflow
    7. Apache Airflow

    AI recommended 7 alternatives but never named GoogleCloudPlatform/ml-design-patterns. 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 GoogleCloudPlatform/ml-design-patterns?
    pass
    AI did not name GoogleCloudPlatform/ml-design-patterns — 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 GoogleCloudPlatform/ml-design-patterns in production, what risks or prerequisites should they evaluate first?
    pass
    AI named GoogleCloudPlatform/ml-design-patterns 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 GoogleCloudPlatform/ml-design-patterns solve, and who is the primary audience?
    pass
    AI did not name GoogleCloudPlatform/ml-design-patterns — 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

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GoogleCloudPlatform/ml-design-patterns — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite