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Comparison

ml-surveys vs Awesome-LLMOps

Verdict

Pick ml-surveys if ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.

Markdown twin · ml-surveys alternatives · Awesome-LLMOps alternatives

GraphCanon updated 1d

ml-surveys logo

ml-surveys

eugeneyan/ml-surveys

★ 2.9kpushed Mar 17, 2023
vs
Awesome-LLMOps logo

Awesome-LLMOps

tensorchord/Awesome-LLMOps

★ 5.9kpushed May 21, 2026

Trust & integrity

Signalml-surveysAwesome-LLMOps
Maintenance
Dormant (1223d since push)
As of 1mo · github_public_v1
Slowing (91d since push)
As of 1d · github_public_v1
Provenance
Not a fork · Personal account
As of 1mo · github_public_v1
Not a fork · Organization account
As of 1d · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
ml-surveys trust reportAwesome-LLMOps trust reportTrust methodology

Tagline

ml-surveys
Survey papers summarizing advances in various AI domains
Awesome-LLMOps
An awesome & curated list of best LLMOps tools for developers

Stars

ml-surveys
2.9k
Awesome-LLMOps
5.9k

Forks

ml-surveys
291
Awesome-LLMOps
993

Open issues

ml-surveys
2
Awesome-LLMOps
247

Language

ml-surveys
-
Awesome-LLMOps
Shell

Adopt for

ml-surveys
ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems.
Awesome-LLMOps
Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.

Persona

ml-surveys
-
Awesome-LLMOps
-

Runtime

ml-surveys
-
Awesome-LLMOps
-

License

ml-surveys
MIT
Awesome-LLMOps
CC0-1.0

Last pushed

ml-surveys
Mar 17, 2023
Awesome-LLMOps
May 21, 2026

Categories

ml-surveys
Computer Vision, Evaluation & Observability, Model Training
Awesome-LLMOps
Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio

Trust and health

Maintenance

ml-surveys
Dormant (18%)
Awesome-LLMOps
Slowing (36%)

Days since push

ml-surveys
1223d
Awesome-LLMOps
91d

Open issues (now)

ml-surveys
2
Awesome-LLMOps
247

Stars delta

ml-surveys
Unknown
Awesome-LLMOps
+28 (30d)

Open issues delta

ml-surveys
Unknown
Awesome-LLMOps
+66 (30d)

Owner type

ml-surveys
User
Awesome-LLMOps
Organization

Full report

ml-surveys
Trust report
Awesome-LLMOps
Trust report

Choose ml-surveys if…

  • License: ml-surveys is MIT, Awesome-LLMOps is CC0-1.0.
  • Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning.
  • When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning

When NOT to use ml-surveys

  • If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary
  • In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
GitHubDetails

Choose Awesome-LLMOps if…

  • License: Awesome-LLMOps is CC0-1.0, ml-surveys is MIT.
  • Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
  • Also covers Data & Retrieval, Inference & Serving, LLM Frameworks, Speech & Audio.
  • - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.

When NOT to use Awesome-LLMOps

  • - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
  • - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
GitHubDetails

Explore

ml-surveys trust report →Awesome-LLMOps trust report →Computer Vision category →Evaluation & Observability category →Model Training category →Data & Retrieval category →Inference & Serving category →LLM Frameworks category →Speech & Audio category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: ml-surveys 2.9k · Awesome-LLMOps 5.9k (synced Jul 22, 2026).

Common questions

What is the difference between ml-surveys and Awesome-LLMOps?
ml-surveys: Survey papers summarizing advances in various AI domains. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
When should I choose ml-surveys over Awesome-LLMOps?
Choose ml-surveys over Awesome-LLMOps when License: ml-surveys is MIT, Awesome-LLMOps is CC0-1.0; Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning; When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning.
When should I choose Awesome-LLMOps over ml-surveys?
Choose Awesome-LLMOps over ml-surveys when License: Awesome-LLMOps is CC0-1.0, ml-surveys is MIT; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Data & Retrieval, Inference & Serving, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When should I avoid ml-surveys?
If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
When should I avoid Awesome-LLMOps?
- When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Is ml-surveys or Awesome-LLMOps more popular on GitHub?
Awesome-LLMOps has more GitHub stars (5,915 vs 2,902). Stars measure visibility, not whether either tool fits your constraints.
Are ml-surveys and Awesome-LLMOps open source?
Yes - both are open-source projects on GitHub (ml-surveys: MIT, Awesome-LLMOps: CC0-1.0).
Where can I find alternatives to ml-surveys or Awesome-LLMOps?
GraphCanon lists graph-backed alternatives at ml-surveys alternatives and Awesome-LLMOps alternatives (ml-surveys markdown twin, Awesome-LLMOps markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, ml-surveys or Awesome-LLMOps?
ml-surveys: Dormant. Awesome-LLMOps: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for ml-surveys and Awesome-LLMOps?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ml-surveys trust report; Awesome-LLMOps trust report.

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