RRepoGEO

REPOGEO REPORT · LITE

determined-ai/determined

Default branch main · commit c1e9c6d7 · scanned 5/17/2026, 4:31:39 PM

GitHub: 3,225 stars · 372 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
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 determined-ai/determined, 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 README opening to emphasize MLOps platform and problem-solving

    Why:

    CURRENT
    Determined is an all-in-one deep learning platform, compatible with PyTorch and TensorFlow.
    COPY-PASTE FIX
    Determined is a comprehensive MLOps platform designed to streamline and scale deep learning workflows, from distributed training and hyperparameter tuning to experiment tracking and GPU resource management. It provides an integrated solution for PyTorch and TensorFlow users.
  • mediumtopics#2
    Add specific topics for experiment tracking and GPU management

    Why:

    CURRENT
    data-science, deep-learning, distributed-training, hyperparameter-optimization, hyperparameter-search, hyperparameter-tuning, keras, kubernetes, machine-learning, ml-infrastructure, ml-platform, mlops, pytorch, tensorflow
    COPY-PASTE FIX
    data-science, deep-learning, distributed-training, experiment-tracking, gpu-management, hyperparameter-optimization, hyperparameter-search, hyperparameter-tuning, keras, kubernetes, machine-learning, ml-infrastructure, ml-platform, mlops, pytorch, tensorflow
  • lowcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives

Determined offers an integrated, opinionated platform for deep learning, distinguishing itself from point solutions. While tools like MLflow, Weights & Biases, and Comet ML excel in specific areas like experiment tracking or logging, Determined provides a unified system for distributed training, hyperparameter tuning, experiment tracking, and GPU resource management, aiming to simplify the entire deep learning workflow from development to production.

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 determined-ai/determined
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Weights & Biases
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Weights & Biases · recommended 2×
  2. MLflow · recommended 2×
  3. Comet ML · recommended 2×
  4. TensorBoard · recommended 2×
  5. ClearML · recommended 2×
  • CATEGORY QUERY
    How can I manage distributed deep learning experiments and optimize hyperparameters efficiently?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases
    2. MLflow
    3. Comet ML
    4. Optuna
    5. Ray Tune
    6. TensorBoard
    7. ClearML

    AI recommended 7 alternatives but never named determined-ai/determined. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What MLOps platform provides robust experiment tracking for PyTorch and TensorFlow models?
    you: not recommended
    AI recommended (in order):
    1. MLflow
    2. Weights & Biases
    3. Comet ML
    4. Neptune.ai
    5. TensorBoard
    6. ClearML

    AI recommended 6 alternatives but never named determined-ai/determined. 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 determined-ai/determined?
    pass
    AI named determined-ai/determined explicitly

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

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

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

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determined-ai/determined — 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