--- title: "awesome-mlops vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelvins-awesome-mlops-vs-visenger-awesome-mlops" tools: ["kelvins-awesome-mlops", "visenger-awesome-mlops"] --- # awesome-mlops vs awesome-mlops *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-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. [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-mlops](https://ml-ops.org) has 14k stars, 2.1k forks, and 44 open issues, last pushed Nov 21, 2024. Figures are from public GitHub metadata via [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | A curated list of awesome MLOps tools. | A curated list of references for MLOps | | Stars | 5,229 | 14,127 | | Forks | 762 | 2,101 | | Open issues | 71 | 44 | | Language | Python | - | | Adopt for | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | - | - | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 97d | 621d | | Open issues (now) | 71 | 44 | | Full report | [trust report](/tools/kelvins-awesome-mlops/trust.md) | [trust report](/tools/visenger-awesome-mlops/trust.md) | ## Shared compatibility - **Python**: [awesome-mlops](/tools/kelvins-awesome-mlops.md) - Python runtime; [awesome-mlops](/tools/visenger-awesome-mlops.md) - Python runtime ## Decision facts: awesome-mlops - **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. ## Decision facts: awesome-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## Choose when ### Choose awesome-mlops if… - Tags unique to awesome-mlops: awesome, machine-learning-engineering, mle. - Also covers Developer Tools, Evaluation & Observability. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### Choose awesome-mlops if… - Tags unique to awesome-mlops: devops, engineering, federated-learning. - If you need references covering online training and inference service architecture patterns, consider awesome-mlops. - More GitHub stars (14k vs 5.2k) - visibility, not fit. ## 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-mlops - Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. - Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance. ## Common questions ### What is the difference between awesome-mlops and awesome-mlops? awesome-mlops: A curated list of awesome MLOps tools.. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-mlops over awesome-mlops? Choose awesome-mlops over awesome-mlops when Tags unique to awesome-mlops: awesome, machine-learning-engineering, mle; Also covers Developer Tools, Evaluation & Observability; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### When should I choose awesome-mlops over awesome-mlops? Choose awesome-mlops over awesome-mlops when Tags unique to awesome-mlops: devops, engineering, federated-learning; If you need references covering online training and inference service architecture patterns, consider awesome-mlops; More GitHub stars (14k vs 5.2k) - visibility, not fit. ### 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-mlops? Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance. ### Is awesome-mlops or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (14,127 vs 5,229). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-mlops and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-mlops or awesome-mlops? GraphCanon lists graph-backed alternatives at [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/alternatives.md), [awesome-mlops markdown twin](/tools/visenger-awesome-mlops/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-visenger-awesome-mlops.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-mlops? awesome-mlops: Slowing. awesome-mlops: 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-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/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/_