--- title: "awesome-production-machine-learning vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethicalml-awesome-production-machine-learning-vs-kelvins-awesome-mlops" tools: ["ethicalml-awesome-production-machine-learning", "kelvins-awesome-mlops"] --- # awesome-production-machine-learning vs awesome-mlops *GraphCanon updated Aug 4, 2026* ## Verdict Pick awesome-production-machine-learning when tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment; pick awesome-mlops when tags unique to awesome-mlops: ai, awesome, data-science, machine-learning. [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-mlops](https://github.com/kelvins/awesome-mlops) has 5.2k stars, 762 forks, and 71 open issues, last pushed Apr 29, 2026. Figures are from public GitHub metadata via [awesome-production-machine-learning's repository](https://github.com/EthicalML/awesome-production-machine-learning) and [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops). | | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Tagline | A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning | A curated list of awesome MLOps tools. | | Stars | 20,821 | 5,229 | | Forks | 2,590 | 762 | | Open issues | 31 | 71 | | Language | - | Python | | Adopt for | - | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | | Persona | - | - | | Runtime | - | - | | License | MIT license making it free for use in both personal and commercial projects without requiring royalty payment or source-code disclosure. | - | | Categories | Data & Retrieval, Evaluation & Observability, Inference & Serving | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 3d | 97d | | Open issues (now) | 31 | 71 | | Owner type | Organization | User | | Full report | [trust report](/tools/ethicalml-awesome-production-machine-learning/trust.md) | [trust report](/tools/kelvins-awesome-mlops/trust.md) | ## Shared compatibility - **Python**: [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) - Python runtime; [awesome-mlops](/tools/kelvins-awesome-mlops.md) - Python runtime ## 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-mlops - **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. ## Choose when ### Choose awesome-production-machine-learning if… - Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment. - Also covers Data & Retrieval. - If you need a diverse set of open-source tools for end-to-end production machine learning tasks ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning. - Also covers Developer Tools, Model Training. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ## 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-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. ## Common questions ### What is the difference between awesome-production-machine-learning and awesome-mlops? awesome-production-machine-learning: A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-production-machine-learning over awesome-mlops? Choose awesome-production-machine-learning over awesome-mlops when Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment; Also covers Data & Retrieval; If you need a diverse set of open-source tools for end-to-end production machine learning tasks. ### When should I choose awesome-mlops over awesome-production-machine-learning? Choose awesome-mlops over awesome-production-machine-learning when Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Developer Tools, Model Training; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### 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-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. ### Is awesome-production-machine-learning or awesome-mlops more popular on GitHub? awesome-production-machine-learning has more GitHub stars (20,821 vs 5,229). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-production-machine-learning and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-production-machine-learning or awesome-mlops? GraphCanon lists graph-backed alternatives at [awesome-production-machine-learning alternatives](/tools/ethicalml-awesome-production-machine-learning/alternatives) and [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) ([awesome-production-machine-learning markdown twin](/tools/ethicalml-awesome-production-machine-learning/alternatives.md), [awesome-mlops markdown twin](/tools/kelvins-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/ethicalml-awesome-production-machine-learning-vs-kelvins-awesome-mlops.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-mlops? awesome-production-machine-learning: Very active. awesome-mlops: 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-mlops? 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-mlops trust report](/tools/kelvins-awesome-mlops/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/_