--- title: "awesome-mlops vs awesome-list-of-awesomes" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelvins-awesome-mlops-vs-nachimak28-awesome-list-of-awesomes" tools: ["kelvins-awesome-mlops", "nachimak28-awesome-list-of-awesomes"] --- # awesome-mlops vs awesome-list-of-awesomes *GraphCanon updated Aug 4, 2026* ## Verdict Pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML; pick awesome-list-of-awesomes if a directory of curated 'awesome lists' on AI topics like ML, DL, CV. [awesome-mlops](https://github.com/kelvins/awesome-mlops) reports 5.2k GitHub stars, 762 forks, and 71 open issues, last pushed Apr 29, 2026. [awesome-list-of-awesomes](https://github.com/Nachimak28/awesome-list-of-awesomes) has 345 stars, 48 forks, and 1 open issues, last pushed Nov 13, 2023. Figures are from public GitHub metadata via [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops) and [awesome-list-of-awesomes's repository](https://github.com/Nachimak28/awesome-list-of-awesomes). | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [awesome-list-of-awesomes](/tools/nachimak28-awesome-list-of-awesomes.md) | | --- | --- | --- | | Tagline | A curated list of awesome MLOps tools. | A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research | | Stars | 5,229 | 345 | | Forks | 762 | 48 | | Open issues | 71 | 1 | | Language | Python | - | | Adopt for | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | A directory of curated 'awesome lists' on AI topics like ML, DL, CV. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | Computer Vision, Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [awesome-list-of-awesomes](/tools/nachimak28-awesome-list-of-awesomes.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 97d | 991d | | Open issues (now) | 71 | 1 | | Full report | [trust report](/tools/kelvins-awesome-mlops/trust.md) | [trust report](/tools/nachimak28-awesome-list-of-awesomes/trust.md) | ## Decision facts: awesome-mlops - **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. ## Decision facts: awesome-list-of-awesomes - **Adopt for:** A directory of curated 'awesome lists' on AI topics like ML, DL, CV. ## Choose when ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml. - Also covers Developer Tools, Inference & Serving. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### Choose awesome-list-of-awesomes if… - Tags unique to awesome-list-of-awesomes: computer-vision, deep-learning, natural-language-processing. - Also covers Computer Vision. - When you need diverse resources covering specific areas in data science and machine learning ## When NOT to use awesome-mlops - In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. - Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings. ## When NOT to use awesome-list-of-awesomes - If you require the latest updates, as not all linked lists are actively maintained - For deeply curated content on new or niche topics not covered ## Common questions ### What is the difference between awesome-mlops and awesome-list-of-awesomes? awesome-mlops: A curated list of awesome MLOps tools.. awesome-list-of-awesomes: A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-mlops over awesome-list-of-awesomes? Choose awesome-mlops over awesome-list-of-awesomes when Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml; Also covers Developer Tools, Inference & Serving; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### When should I choose awesome-list-of-awesomes over awesome-mlops? Choose awesome-list-of-awesomes over awesome-mlops when Tags unique to awesome-list-of-awesomes: computer-vision, deep-learning, natural-language-processing; Also covers Computer Vision; When you need diverse resources covering specific areas in data science and machine learning. ### When should I avoid awesome-mlops? In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings. ### When should I avoid awesome-list-of-awesomes? If you require the latest updates, as not all linked lists are actively maintained For deeply curated content on new or niche topics not covered ### Is awesome-mlops or awesome-list-of-awesomes more popular on GitHub? awesome-mlops has more GitHub stars (5,229 vs 345). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-mlops and awesome-list-of-awesomes open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-mlops or awesome-list-of-awesomes? GraphCanon lists graph-backed alternatives at [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) and [awesome-list-of-awesomes alternatives](/tools/nachimak28-awesome-list-of-awesomes/alternatives) ([awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/alternatives.md), [awesome-list-of-awesomes markdown twin](/tools/nachimak28-awesome-list-of-awesomes/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/kelvins-awesome-mlops-vs-nachimak28-awesome-list-of-awesomes.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-mlops or awesome-list-of-awesomes? awesome-mlops: Slowing. awesome-list-of-awesomes: Dormant. 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-mlops and awesome-list-of-awesomes? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust); [awesome-list-of-awesomes trust report](/tools/nachimak28-awesome-list-of-awesomes/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kelvins-awesome-mlops`](/api/graphcanon/graph?tool=kelvins-awesome-mlops) - 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/_