RRepoGEO

REPOGEO REPORT · LITE

alirezadir/Production-Level-Deep-Learning

Default branch master · commit cc393609 · scanned 6/20/2026, 2:38:34 PM

GitHub: 4,645 stars · 683 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
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 alirezadir/Production-Level-Deep-Learning, 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
  • highabout#1
    Reposition the About description to clarify repo's nature

    Why:

    CURRENT
    A guideline for building practical production-level deep learning systems to be deployed in real world applications.
    COPY-PASTE FIX
    A comprehensive, curated engineering guideline and resource for designing, building, and deploying robust production-level deep learning systems, focusing on MLOps best practices and system architecture rather than specific tools or frameworks.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root. A common choice for educational or guideline repositories is the MIT License or Apache-2.0, which are permissive and widely recognized.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a homepage URL in the repository settings. This could link to a dedicated project website, a more detailed documentation page, or even a relevant section within the README if no external site exists.

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 alirezadir/Production-Level-Deep-Learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
mlflow/mlflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. mlflow/mlflow · recommended 2×
  2. docker/docker · recommended 1×
  3. onnx/onnx · recommended 1×
  4. kubernetes/kubernetes · recommended 1×
  5. tensorflow/serving · recommended 1×
  • CATEGORY QUERY
    What are best practices for deploying deep learning models into production?
    you: not recommended
    AI recommended (in order):
    1. MLflow (mlflow/mlflow)
    2. Docker (docker/docker)
    3. ONNX (onnx/onnx)
    4. Kubernetes (kubernetes/kubernetes)
    5. TensorFlow Serving (tensorflow/serving)
    6. TorchServe (pytorch/serve)
    7. NVIDIA Triton Inference Server (triton-inference-server/server)
    8. AWS SageMaker
    9. Google Cloud Vertex AI
    10. Prometheus (prometheus/prometheus)
    11. Grafana (grafana/grafana)
    12. Datadog
    13. New Relic
    14. Fiddler AI
    15. Arize AI
    16. GitHub Actions
    17. GitLab CI/CD
    18. Jenkins (jenkinsci/jenkins)
    19. Kubeflow Pipelines (kubeflow/pipelines)
    20. DVC (Data Version Control) (iterative/dvc)
    21. Vault (HashiCorp) (hashicorp/vault)
    22. OWASP ZAP (zaproxy/zaproxy)
    23. Burp Suite

    AI recommended 23 alternatives but never named alirezadir/Production-Level-Deep-Learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to design scalable machine learning systems for real-world applications?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Extended (TFX) (tensorflow/tfx)
    2. Kubeflow (kubeflow/kubeflow)
    3. MLflow (mlflow/mlflow)
    4. Apache Spark (apache/spark)
    5. Ray (ray-project/ray)
    6. Dask (dask/dask)
    7. Metaflow (Netflix/metaflow)

    AI recommended 7 alternatives but never named alirezadir/Production-Level-Deep-Learning. 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 alirezadir/Production-Level-Deep-Learning?
    pass
    AI did not name alirezadir/Production-Level-Deep-Learning — 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 alirezadir/Production-Level-Deep-Learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named alirezadir/Production-Level-Deep-Learning 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 alirezadir/Production-Level-Deep-Learning solve, and who is the primary audience?
    pass
    AI did not name alirezadir/Production-Level-Deep-Learning — 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

Drop this badge into the README of alirezadir/Production-Level-Deep-Learning. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/alirezadir/Production-Level-Deep-Learning.svg)](https://repogeo.com/en/r/alirezadir/Production-Level-Deep-Learning)
HTML
<a href="https://repogeo.com/en/r/alirezadir/Production-Level-Deep-Learning"><img src="https://repogeo.com/badge/alirezadir/Production-Level-Deep-Learning.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

alirezadir/Production-Level-Deep-Learning — 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