RRepoGEO

REPOGEO REPORT · LITE

aws/sagemaker-python-sdk

Default branch master · commit a8011a46 · scanned 6/24/2026, 12:26:47 PM

GitHub: 2,248 stars · 1,274 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
40 /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
3 / 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 aws/sagemaker-python-sdk, 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's opening paragraph to clarify its role as the official SDK for SageMaker's managed service

    Why:

    CURRENT
    SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker.
    COPY-PASTE FIX
    The SageMaker Python SDK is the official open-source library for Amazon SageMaker, providing a high-level Python interface to train and deploy machine learning models on Amazon's fully managed ML service.
  • mediumtopics#2
    Add more specific topics related to MLOps, model deployment, and cloud ML

    Why:

    CURRENT
    aws, huggingface, machine-learning, mxnet, python, pytorch, sagemaker, tensorflow
    COPY-PASTE FIX
    aws, huggingface, machine-learning, mxnet, python, pytorch, sagemaker, tensorflow, mlops, model-deployment, cloud-ml, deep-learning
  • lowabout#3
    Refine the 'About' description to emphasize its role as the official Python SDK for SageMaker

    Why:

    CURRENT
    A library for training and deploying machine learning models on Amazon SageMaker
    COPY-PASTE FIX
    The official Python SDK for Amazon SageMaker, simplifying the training and deployment of machine learning models on the managed service.

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 aws/sagemaker-python-sdk
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Vertex AI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Vertex AI · recommended 1×
  2. Amazon SageMaker · recommended 1×
  3. Azure Machine Learning · recommended 1×
  4. Databricks Machine Learning · recommended 1×
  5. Hugging Face Inference Endpoints · recommended 1×
  • CATEGORY QUERY
    How to train and deploy machine learning models using Python on a managed service?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Vertex AI
    2. Amazon SageMaker
    3. Azure Machine Learning
    4. Databricks Machine Learning
    5. Hugging Face Inference Endpoints
    6. Weights & Biases (W&B) (wandb/wandb)

    AI recommended 6 alternatives but never named aws/sagemaker-python-sdk. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Python library for deploying deep learning models from popular frameworks to a scalable environment?
    you: not recommended
    AI recommended (in order):
    1. MLflow
    2. Ray Serve
    3. BentoML
    4. TensorFlow Serving
    5. TorchServe
    6. KServe

    AI recommended 6 alternatives but never named aws/sagemaker-python-sdk. 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 aws/sagemaker-python-sdk?
    pass
    AI named aws/sagemaker-python-sdk explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts aws/sagemaker-python-sdk in production, what risks or prerequisites should they evaluate first?
    pass
    AI named aws/sagemaker-python-sdk 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 aws/sagemaker-python-sdk solve, and who is the primary audience?
    pass
    AI named aws/sagemaker-python-sdk explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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