--- title: "awesome-automl-papers vs Failed-ML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hibayesian-awesome-automl-papers-vs-kennethleungty-failed-ml" tools: ["hibayesian-awesome-automl-papers", "kennethleungty-failed-ml"] --- # awesome-automl-papers vs Failed-ML *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 Failed-ML if failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning systems. [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. [Failed-ML](https://towardsdatascience.com/when-ai-goes-astray-high-profile-machine-learning-mishaps-in-the-real-world-26bd58692195) has 753 stars, 51 forks, and 0 open issues, last pushed Jun 14, 2024. Figures are from public GitHub metadata via [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers) and [Failed-ML's repository](https://github.com/kennethleungty/Failed-ML). | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [Failed-ML](/tools/kennethleungty-failed-ml.md) | | --- | --- | --- | | Tagline | A curated list of automated machine learning papers and resources. | Compilation of high-profile real-world examples of failed machine learning projects | | Stars | 4,155 | 753 | | Forks | 678 | 51 | | Open issues | 2 | 0 | | Language | - | - | | 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. | Failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning systems. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [Failed-ML](/tools/kennethleungty-failed-ml.md) | | --- | --- | --- | | Days since push | 784d | 777d | | Open issues (now) | 2 | 0 | | Full report | [trust report](/tools/hibayesian-awesome-automl-papers/trust.md) | [trust report](/tools/kennethleungty-failed-ml/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: Failed-ML - **Hosting:** self hosted - **Pricing:** freemium - Open source under MIT license but no additional paid features are mentioned. - **Requirements:** Not a software tool that requires installation. Informational repository intended for reading and learning. - **Adopt for:** Failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning systems. ## Choose when ### Choose awesome-automl-papers if… - License: awesome-automl-papers is Apache-2.0, Failed-ML is MIT. - Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search. - Also covers Model Training. - When you need a curated list of academic materials to research or learn about AutoML technologies ### Choose Failed-ML if… - License: Failed-ML is MIT, awesome-automl-papers is Apache-2.0. - Pricing: Open source under MIT license but no additional paid features are mentioned.. - Requirements: Not a software tool that requires installation. Informational repository intended for reading and learning.. - Tags unique to Failed-ML: ai, artificial-intelligence, classification, computer-vision. - When you seek specific historical examples of where machine learning application went wrong, aiding in understanding the potential mistakes and challenges one might face. ## 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 Failed-ML - If you need a prescriptive guide for solving your current project's technical problems rather than learning from industry-wide mistakes. - When looking for detailed quantitative metrics on failed projects, Failed-ML focuses more on high-level analysis of failure causes rather than specific performance numbers. ## Common questions ### What is the difference between awesome-automl-papers and Failed-ML? awesome-automl-papers: A curated list of automated machine learning papers and resources.. Failed-ML: Compilation of high-profile real-world examples of failed machine learning projects. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-automl-papers over Failed-ML? Choose awesome-automl-papers over Failed-ML when License: awesome-automl-papers is Apache-2.0, Failed-ML is MIT; Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; Also covers Model Training; When you need a curated list of academic materials to research or learn about AutoML technologies. ### When should I choose Failed-ML over awesome-automl-papers? Choose Failed-ML over awesome-automl-papers when License: Failed-ML is MIT, awesome-automl-papers is Apache-2.0; Pricing: Open source under MIT license but no additional paid features are mentioned.; Requirements: Not a software tool that requires installation. Informational repository intended for reading and learning.; Tags unique to Failed-ML: ai, artificial-intelligence, classification, computer-vision; When you seek specific historical examples of where machine learning application went wrong, aiding in understanding the potential mistakes and challenges one might face. ### 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 Failed-ML? If you need a prescriptive guide for solving your current project's technical problems rather than learning from industry-wide mistakes. When looking for detailed quantitative metrics on failed projects, Failed-ML focuses more on high-level analysis of failure causes rather than specific performance numbers. ### Is awesome-automl-papers or Failed-ML more popular on GitHub? awesome-automl-papers has more GitHub stars (4,155 vs 753). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-automl-papers and Failed-ML open source? Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, Failed-ML: MIT). ### Where can I find alternatives to awesome-automl-papers or Failed-ML? GraphCanon lists graph-backed alternatives at [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) and [Failed-ML alternatives](/tools/kennethleungty-failed-ml/alternatives) ([awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/alternatives.md), [Failed-ML markdown twin](/tools/kennethleungty-failed-ml/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-kennethleungty-failed-ml.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 Failed-ML? awesome-automl-papers: Dormant. Failed-ML: 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-automl-papers and Failed-ML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/trust); [Failed-ML trust report](/tools/kennethleungty-failed-ml/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/_