--- title: "awesome-automl-papers vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hibayesian-awesome-automl-papers-vs-kelvins-awesome-mlops" tools: ["hibayesian-awesome-automl-papers", "kelvins-awesome-mlops"] --- # awesome-automl-papers vs awesome-mlops *GraphCanon updated Aug 4, 2026* ## Verdict Pick awesome-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search; pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) reports 4.2k GitHub stars, 678 forks, and 2 open issues, last pushed Jun 11, 2024. [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-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers) and [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops). | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Tagline | A curated list of automated machine learning papers and resources. | A curated list of awesome MLOps tools. | | Stars | 4,155 | 5,229 | | Forks | 678 | 762 | | Open issues | 2 | 71 | | Language | - | Python | | Adopt for | awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Evaluation & Observability, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 784d | 97d | | Open issues (now) | 2 | 71 | | Full report | [trust report](/tools/hibayesian-awesome-automl-papers/trust.md) | [trust report](/tools/kelvins-awesome-mlops/trust.md) | ## Decision facts: awesome-automl-papers - **Adopt for:** awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. ## 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-automl-papers if… - Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search. - When you need a curated list of academic materials to research or learn about AutoML technologies - Leaner open-issue backlog (2). ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning. - Also covers Developer Tools, Inference & Serving. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ## When NOT to use awesome-automl-papers - If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources - When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ## 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-automl-papers and awesome-mlops? awesome-automl-papers: A curated list of automated machine learning papers and resources.. 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-automl-papers over awesome-mlops? Choose awesome-automl-papers over awesome-mlops when Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; When you need a curated list of academic materials to research or learn about AutoML technologies; Leaner open-issue backlog (2). ### When should I choose awesome-mlops over awesome-automl-papers? Choose awesome-mlops over awesome-automl-papers when Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; 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 avoid awesome-automl-papers? If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers ### 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-automl-papers or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (5,229 vs 4,155). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-automl-papers and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-automl-papers or awesome-mlops? GraphCanon lists graph-backed alternatives at [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) and [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) ([awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/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/hibayesian-awesome-automl-papers-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-automl-papers or awesome-mlops? awesome-automl-papers: 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-automl-papers and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/trust); [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers`](/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers) - 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/_