--- title: "awesome-production-machine-learning vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethicalml-awesome-production-machine-learning-vs-tensorchord-awesome-llmops" tools: ["ethicalml-awesome-production-machine-learning", "tensorchord-awesome-llmops"] --- # awesome-production-machine-learning vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick awesome-production-machine-learning when license: awesome-production-machine-learning is MIT, Awesome-LLMOps is CC0-1.0; pick Awesome-LLMOps when license: Awesome-LLMOps is CC0-1.0, awesome-production-machine-learning is MIT. [awesome-production-machine-learning](https://ethicalml.github.io/awesome-production-machine-learning) reports 21k GitHub stars, 2.6k forks, and 31 open issues, last pushed Aug 1, 2026. [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 [awesome-production-machine-learning's repository](https://github.com/EthicalML/awesome-production-machine-learning) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning | An awesome & curated list of best LLMOps tools for developers | | Stars | 20,821 | 5,915 | | Forks | 2,590 | 993 | | Open issues | 31 | 247 | | Language | - | Shell | | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT license making it free for use in both personal and commercial projects without requiring royalty payment or source-code disclosure. | CC0-1.0 | | Categories | Data & Retrieval, Evaluation & Observability, Inference & Serving | 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._ | | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 3d | 91d | | Open issues (now) | 31 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/ethicalml-awesome-production-machine-learning/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: awesome-production-machine-learning - **License detail:** MIT license making it free for use in both personal and commercial projects without requiring royalty payment or source-code disclosure. ## 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 awesome-production-machine-learning if… - License: awesome-production-machine-learning is MIT, Awesome-LLMOps is CC0-1.0. - Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment. - If you need a diverse set of open-source tools for end-to-end production machine learning tasks ### Choose Awesome-LLMOps if… - License: Awesome-LLMOps is CC0-1.0, awesome-production-machine-learning is MIT. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, LLM Frameworks, Model Training, 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-production-machine-learning - If you seek a comprehensive solution integrated into one platform rather than selecting from diverse tools - When your project is specific to only one aspect of machine learning like just deployment or monitoring, and not for the entire workflow - For teams preferring vendor-specific solutions over open-source options ## 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 awesome-production-machine-learning and Awesome-LLMOps? awesome-production-machine-learning: A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning. 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 awesome-production-machine-learning over Awesome-LLMOps? Choose awesome-production-machine-learning over Awesome-LLMOps when License: awesome-production-machine-learning is MIT, Awesome-LLMOps is CC0-1.0; Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment; If you need a diverse set of open-source tools for end-to-end production machine learning tasks. ### When should I choose Awesome-LLMOps over awesome-production-machine-learning? Choose Awesome-LLMOps over awesome-production-machine-learning when License: Awesome-LLMOps is CC0-1.0, awesome-production-machine-learning is MIT; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, LLM Frameworks, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### When should I avoid awesome-production-machine-learning? If you seek a comprehensive solution integrated into one platform rather than selecting from diverse tools When your project is specific to only one aspect of machine learning like just deployment or monitoring, and not for the entire workflow For teams preferring vendor-specific solutions over open-source options ### 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 awesome-production-machine-learning or Awesome-LLMOps more popular on GitHub? awesome-production-machine-learning has more GitHub stars (20,821 vs 5,915). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-production-machine-learning and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (awesome-production-machine-learning: MIT, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to awesome-production-machine-learning or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [awesome-production-machine-learning alternatives](/tools/ethicalml-awesome-production-machine-learning/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([awesome-production-machine-learning markdown twin](/tools/ethicalml-awesome-production-machine-learning/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/ethicalml-awesome-production-machine-learning-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, awesome-production-machine-learning or Awesome-LLMOps? awesome-production-machine-learning: Very active. 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 awesome-production-machine-learning and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-production-machine-learning trust report](/tools/ethicalml-awesome-production-machine-learning/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ethicalml-awesome-production-machine-learning`](/api/graphcanon/graph?tool=ethicalml-awesome-production-machine-learning) - 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/_