Comparison
awesome-automl-papers vs Failed-ML
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.
Markdown twin · awesome-automl-papers alternatives · Failed-ML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-automl-papers | Failed-ML |
|---|---|---|
| Maintenance | Dormant (784d since push) As of 2w · github_public_v1 | Dormant (777d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- awesome-automl-papers
- 4.2k
- Failed-ML
- 753
Forks
- awesome-automl-papers
- 678
- Failed-ML
- 51
Open issues
- awesome-automl-papers
- 2
- Failed-ML
- 0
Language
- awesome-automl-papers
- -
- Failed-ML
- -
Adopt for
- awesome-automl-papers
- 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
- 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
- awesome-automl-papers
- -
- Failed-ML
- -
Runtime
- awesome-automl-papers
- -
- Failed-ML
- -
License
- awesome-automl-papers
- Apache-2.0
- Failed-ML
- MIT
Last pushed
- awesome-automl-papers
- Jun 11, 2024
- Failed-ML
- Jun 14, 2024
Categories
- awesome-automl-papers
- Evaluation & Observability, Model Training
- Failed-ML
- Evaluation & Observability
Trust and health
Days since push
- awesome-automl-papers
- 784d
- Failed-ML
- 777d
Open issues (now)
- awesome-automl-papers
- 2
- Failed-ML
- 0
Full report
- awesome-automl-papers
- Trust report
- Failed-ML
- Trust report
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
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (hibayesian/awesome-automl-papers) · observed Aug 4, 2026
- GitHub forks (hibayesian/awesome-automl-papers) · observed Aug 4, 2026
- Last push (hibayesian/awesome-automl-papers) · observed Jun 11, 2024
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (kennethleungty/Failed-ML) · observed Jul 31, 2026
- GitHub forks (kennethleungty/Failed-ML) · observed Jul 31, 2026
- Last push (kennethleungty/Failed-ML) · observed Jun 14, 2024
- License file (MIT) · observed Jul 31, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-automl-papers 4.2k · Failed-ML 753 (synced Aug 4, 2026).
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 and Failed-ML alternatives (awesome-automl-papers markdown twin, Failed-ML markdown twin), 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 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; Failed-ML trust report.