--- title: "Awesome-AutoDL vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-kelvins-awesome-mlops" tools: ["d-x-y-awesome-autodl", "kelvins-awesome-mlops"] --- # Awesome-AutoDL vs awesome-mlops *GraphCanon updated Aug 4, 2026* ## Verdict Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. [Awesome-AutoDL](https://github.com/D-X-Y/Awesome-AutoDL) reports 2.3k GitHub stars, 319 forks, and 2 open issues, last pushed Sep 26, 2022. [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-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | A curated list of awesome MLOps tools. | | Stars | 2,339 | 5,229 | | Forks | 319 | 762 | | Open issues | 2 | 71 | | Language | Python | Python | | Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | | Persona | - | - | | Runtime | - | - | | License | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | - | | 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._ | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1408d | 97d | | Open issues (now) | 2 | 71 | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/kelvins-awesome-mlops/trust.md) | ## Decision facts: Awesome-AutoDL - **Adopt for:** A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. - **License detail:** MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. ## 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-AutoDL if… - Tags unique to Awesome-AutoDL: autodl, automl, deep-learning, hyper-parameter-optimization. - Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS). - Leaner open-issue backlog (2). ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, data-science, 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 Awesome-AutoDL - Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. - Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide. ## 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-AutoDL and awesome-mlops? Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. 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-AutoDL over awesome-mlops? Choose Awesome-AutoDL over awesome-mlops when Tags unique to Awesome-AutoDL: autodl, automl, deep-learning, hyper-parameter-optimization; Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS); Leaner open-issue backlog (2). ### When should I choose awesome-mlops over Awesome-AutoDL? Choose awesome-mlops over Awesome-AutoDL when Tags unique to awesome-mlops: ai, data-science, 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 Awesome-AutoDL? Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide. ### 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-AutoDL or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (5,229 vs 2,339). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AutoDL and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-AutoDL or awesome-mlops? GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/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/d-x-y-awesome-autodl-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-AutoDL or awesome-mlops? Awesome-AutoDL: Dormant. 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-AutoDL and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=d-x-y-awesome-autodl`](/api/graphcanon/graph?tool=d-x-y-awesome-autodl) - 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/_