RRepoGEO

REPOGEO REPORT · LITE

databricks/mlops-stacks

Default branch main · commit 1c87ae24 · scanned 6/3/2026, 12:06:50 PM

GitHub: 687 stars · 257 forks

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 databricks/mlops-stacks, 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 sentence to emphasize "project template" and "IaC stack"

    Why:

    CURRENT
    This repo provides a customizable stack for starting new ML projects on Databricks that follow production best-practices out of the box.
    COPY-PASTE FIX
    This repository offers a customizable, production-ready project template and infrastructure-as-code (IaC) stack for quickly starting new ML projects on Databricks, pre-configured with best practices.
  • mediumtopics#2
    Expand GitHub topics to include more specific keywords

    Why:

    CURRENT
    databricks, machine-learning, mlops
    COPY-PASTE FIX
    databricks, machine-learning, mlops, project-template, starter-kit, ci-cd, infrastructure-as-code, terraform
  • lowreadme#3
    Add a dedicated "Who is this for?" section to the README

    Why:

    COPY-PASTE FIX
    ## Who is this for?
This MLOps Stack is designed for:
*   **Data Scientists** looking to quickly start new ML projects with production-grade setup.
*   **ML Engineers** aiming to standardize MLOps practices and automate CI/CD for ML workflows on Databricks.
*   **Platform Teams** seeking a robust template for provisioning new data science projects with pre-configured infrastructure and best practices.

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 databricks/mlops-stacks
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. iterative/dvc · recommended 2×
  3. Netflix/metaflow · recommended 1×
  4. kubeflow/kubeflow · recommended 1×
  5. tiangolo/fastapi · recommended 1×
  • CATEGORY QUERY
    How to quickly start new machine learning projects with production best practices?
    you: not recommended
    AI recommended (in order):
    1. MLflow (mlflow/mlflow)
    2. Metaflow (Netflix/metaflow)
    3. Kubeflow (kubeflow/kubeflow)
    4. DVC (iterative/dvc)
    5. FastAPI (tiangolo/fastapi)
    6. BentoML (bentoml/BentoML)
    7. Seldon Core (SeldonIO/seldon-core)
    8. Weights & Biases
    9. Hugging Face Transformers (huggingface/transformers)
    10. Accelerate (huggingface/accelerate)

    AI recommended 10 alternatives but never named databricks/mlops-stacks. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good tools for automating MLOps CI/CD pipelines in machine learning projects?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow Pipelines (kubeflow/pipelines)
    2. MLflow (mlflow/mlflow)
    3. GitHub Actions
    4. GitLab CI/CD
    5. Azure DevOps Pipelines
    6. Jenkins (jenkinsci/jenkins)
    7. Argo Workflows (argoproj/argo-workflows)
    8. DVC (Data Version Control) (iterative/dvc)

    AI recommended 8 alternatives but never named databricks/mlops-stacks. 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 databricks/mlops-stacks?
    pass
    AI named databricks/mlops-stacks explicitly

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

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

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

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