Comparison
100-AI-Machine-Learning-Deep-Learnin-Projects vs Failed-ML
Verdict
Pick 100-AI-Machine-Learning-Deep-Learnin-Projects if 100-AI-Machine-Learning-Deep-Learnin-Projects is a curated repository of 100 production-ready AI solutions, covering a wide range of areas including computer vision, NLP, and deep learning, with a focus on real-world, in; 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.
Markdown twin · 100-AI-Machine-Learning-Deep-Learnin-Projects alternatives · Failed-ML alternatives
GraphCanon updated Sep 30, 2026
100-AI-Machine-Learning-Deep-Learnin-Projects
AdilShamim8/100-AI-Machine-Learning-Deep-Learnin-Projects
★ 277pushed Sep 21, 2026
Trust & integrity
| Signal | 100-AI-Machine-Learning-Deep-Learnin-Projects | Failed-ML |
|---|---|---|
| Maintenance | Very active (3d since push) As of Sep 25, 2026 · github_public_v1 | Dormant (830d since push) As of Sep 23, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 25, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 23, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- A curated, production-grade collection of 100 AI, Machine Learning, and Deep Learning projects
- Failed-ML
- Compilation of high-profile real-world examples of failed machine learning projects
Stars
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- 277
- Failed-ML
- 751
Forks
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- 23
- Failed-ML
- 52
Open issues
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- 0
- Failed-ML
- 0
Language
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- HTML
- Failed-ML
- -
Adopt for
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- 100-AI-Machine-Learning-Deep-Learnin-Projects is a curated repository of 100 production-ready AI solutions, covering a wide range of areas including computer vision, NLP, and deep learning, with a focus on real-world, in
- 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
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- -
- Failed-ML
- -
Runtime
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- -
- Failed-ML
- -
License
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- The license for this repository is unknown, which may pose risks for users who need to ensure compliance with specific licensing requirements.
- Failed-ML
- MIT
Last pushed
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- Sep 21, 2026
- Failed-ML
- Jun 14, 2024
Categories
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- Failed-ML
- Evaluation & Observability
Trust and health
Maintenance
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- Very active (96%)
- Failed-ML
- Dormant (18%)
Days since push
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- 3d
- Failed-ML
- 830d
Stars delta
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- +35 (30d)
- Failed-ML
- -2 (30d)
Full report
- 100-AI-Machine-Learning-Deep-Learnin-Projects
- Trust report
- Failed-ML
- Trust report
Choose 100-AI-Machine-Learning-Deep-Learnin-Projects if…
- Requirements: The repository is primarily focused on Python-based projects, so proficiency in Python is recommended..
- Tags unique to 100-AI-Machine-Learning-Deep-Learnin-Projects: computer-vision-projects, deep-learning-projects, machine-learning.
- Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Model Training.
- When you need a comprehensive collection of production-grade AI projects that span a decade of real-world experience.
When NOT to use 100-AI-Machine-Learning-Deep-Learnin-Projects
- If you are seeking a repository with a focus on theoretical research rather than practical, production-ready solutions.
- When you need a repository that is actively maintained with a clear license, as this repository's license status is unknown.
- If you require a tool that supports a specific programming language other than Python, as the repository primarily focuses on Python-based projects.
Choose Failed-ML if…
- 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: classification, data-engineering, data-quality.
- 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 (AdilShamim8/100-AI-Machine-Learning-Deep-Learnin-Projects) · observed Sep 25, 2026
- GitHub forks (AdilShamim8/100-AI-Machine-Learning-Deep-Learnin-Projects) · observed Sep 25, 2026
- Last push (AdilShamim8/100-AI-Machine-Learning-Deep-Learnin-Projects) · observed Sep 21, 2026
- License file (unknown) · observed Sep 25, 2026
- Decision facts (enrichment) · observed Sep 30, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (kennethleungty/Failed-ML) · observed Sep 23, 2026
- GitHub forks (kennethleungty/Failed-ML) · observed Sep 23, 2026
- Last push (kennethleungty/Failed-ML) · observed Jun 14, 2024
- License file (MIT) · observed Sep 23, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: 100-AI-Machine-Learning-Deep-Learnin-Projects 277 · Failed-ML 751 (synced Sep 25, 2026).
Common questions
- What is the difference between 100-AI-Machine-Learning-Deep-Learnin-Projects and Failed-ML?
- 100-AI-Machine-Learning-Deep-Learnin-Projects: A curated, production-grade collection of 100 AI, Machine Learning, and Deep Learning projects. 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 100-AI-Machine-Learning-Deep-Learnin-Projects over Failed-ML?
- Choose 100-AI-Machine-Learning-Deep-Learnin-Projects over Failed-ML when Requirements: The repository is primarily focused on Python-based projects, so proficiency in Python is recommended.; Tags unique to 100-AI-Machine-Learning-Deep-Learnin-Projects: computer-vision-projects, deep-learning-projects, machine-learning; Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Model Training; When you need a comprehensive collection of production-grade AI projects that span a decade of real-world experience.
- When should I choose Failed-ML over 100-AI-Machine-Learning-Deep-Learnin-Projects?
- Choose Failed-ML over 100-AI-Machine-Learning-Deep-Learnin-Projects when 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: classification, data-engineering, data-quality; 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 100-AI-Machine-Learning-Deep-Learnin-Projects?
- If you are seeking a repository with a focus on theoretical research rather than practical, production-ready solutions. When you need a repository that is actively maintained with a clear license, as this repository's license status is unknown. If you require a tool that supports a specific programming language other than Python, as the repository primarily focuses on Python-based projects.
- 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 100-AI-Machine-Learning-Deep-Learnin-Projects or Failed-ML more popular on GitHub?
- Failed-ML has more GitHub stars (751 vs 277). Stars measure visibility, not whether either tool fits your constraints.
- Are 100-AI-Machine-Learning-Deep-Learnin-Projects and Failed-ML open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to 100-AI-Machine-Learning-Deep-Learnin-Projects or Failed-ML?
- GraphCanon lists graph-backed alternatives at 100-AI-Machine-Learning-Deep-Learnin-Projects alternatives and Failed-ML alternatives (100-AI-Machine-Learning-Deep-Learnin-Projects 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, 100-AI-Machine-Learning-Deep-Learnin-Projects or Failed-ML?
- 100-AI-Machine-Learning-Deep-Learnin-Projects: Very active. 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 100-AI-Machine-Learning-Deep-Learnin-Projects and Failed-ML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: 100-AI-Machine-Learning-Deep-Learnin-Projects trust report; Failed-ML trust report.