--- title: "autogluon vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autogluon-autogluon-vs-kelvins-awesome-mlops" tools: ["autogluon-autogluon", "kelvins-awesome-mlops"] --- # autogluon vs awesome-mlops *GraphCanon updated Aug 4, 2026* ## Verdict Pick autogluon if autoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP; pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. [autogluon](https://auto.gluon.ai/) reports 11k GitHub stars, 1.2k forks, and 388 open issues, last pushed Aug 3, 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 [autogluon's repository](https://github.com/autogluon/autogluon) and [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops). | | [autogluon](/tools/autogluon-autogluon.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Tagline | Fast and Accurate ML in 3 Lines of Code | A curated list of awesome MLOps tools. | | Stars | 10,576 | 5,229 | | Forks | 1,171 | 762 | | Open issues | 388 | 71 | | Language | Python | Python | | Adopt for | AutoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP. | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors. | - | | Categories | Developer Tools, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autogluon](/tools/autogluon-autogluon.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 97d | | Open issues (now) | 388 | 71 | | Owner type | Organization | User | | Full report | [trust report](/tools/autogluon-autogluon/trust.md) | [trust report](/tools/kelvins-awesome-mlops/trust.md) | ## Shared compatibility - **Python**: [autogluon](/tools/autogluon-autogluon.md) - Python runtime; [awesome-mlops](/tools/kelvins-awesome-mlops.md) - Python runtime ## Decision facts: autogluon - **Adopt for:** AutoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP. - **License detail:** Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors. ## Decision facts: awesome-mlops - **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. ## Choose when ### Choose autogluon if… - Tags unique to autogluon: automated-machine-learning, automl, computer-vision, deep-learning. - When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis. - More GitHub stars (11k vs 5.2k) - visibility, not fit. ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, awesome, machine-learning, machine-learning-engineering. - Also covers Evaluation & Observability, Inference & Serving. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ## When NOT to use autogluon - If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation. - For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this. ## 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 autogluon and awesome-mlops? autogluon: Fast and Accurate ML in 3 Lines of Code. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories. ### When should I choose autogluon over awesome-mlops? Choose autogluon over awesome-mlops when Tags unique to autogluon: automated-machine-learning, automl, computer-vision, deep-learning; When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis; More GitHub stars (11k vs 5.2k) - visibility, not fit. ### When should I choose awesome-mlops over autogluon? Choose awesome-mlops over autogluon when Tags unique to awesome-mlops: ai, awesome, machine-learning, machine-learning-engineering; Also covers Evaluation & Observability, Inference & Serving; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### When should I avoid autogluon? If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation. For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this. ### 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 autogluon or awesome-mlops more popular on GitHub? autogluon has more GitHub stars (10,576 vs 5,229). Stars measure visibility, not whether either tool fits your constraints. ### Are autogluon and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to autogluon or awesome-mlops? GraphCanon lists graph-backed alternatives at [autogluon alternatives](/tools/autogluon-autogluon/alternatives) and [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) ([autogluon markdown twin](/tools/autogluon-autogluon/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/autogluon-autogluon-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, autogluon or awesome-mlops? autogluon: 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 autogluon and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autogluon trust report](/tools/autogluon-autogluon/trust); [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=autogluon-autogluon`](/api/graphcanon/graph?tool=autogluon-autogluon) - 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/_