--- title: "ml-surveys vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eugeneyan-ml-surveys-vs-tensorchord-awesome-llmops" tools: ["eugeneyan-ml-surveys", "tensorchord-awesome-llmops"] --- # ml-surveys vs Awesome-LLMOps *GraphCanon updated Aug 22, 2026* ## 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. [ml-surveys](https://github.com/eugeneyan/ml-surveys) reports 2.9k GitHub stars, 292 forks, and 2 open issues, last pushed Mar 17, 2023. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 993 forks, and 247 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [ml-surveys's repository](https://github.com/eugeneyan/ml-surveys) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Survey papers summarizing advances in various AI domains | An awesome & curated list of best LLMOps tools for developers | | Stars | 2,902 | 5,915 | | Forks | 292 | 993 | | Open issues | 2 | 247 | | Language | - | Shell | | Adopt for | 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 is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. | | Persona | - | - | | Runtime | - | - | | License | MIT | CC0-1.0 | | Categories | Computer Vision, Evaluation & Observability, Model Training | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1254d | 91d | | Open issues (now) | 2 | 247 | | Stars delta | 0 (30d) | +28 (30d) | | Open issues delta | 0 (30d) | +66 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/eugeneyan-ml-surveys/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: ml-surveys - **Adopt for:** 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. ## Decision facts: Awesome-LLMOps - **Adopt for:** 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. ## Choose when ### 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 ### 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 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 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. ## 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](/tools/eugeneyan-ml-surveys/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([ml-surveys markdown twin](/tools/eugeneyan-ml-surveys/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/alternatives.md)), 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](/compare/eugeneyan-ml-surveys-vs-tensorchord-awesome-llmops.md) 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](/tools/eugeneyan-ml-surveys/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eugeneyan-ml-surveys`](/api/graphcanon/graph?tool=eugeneyan-ml-surveys) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_