--- title: "100-AI-Machine-Learning-Deep-Learnin-Projects vs Failed-ML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/adilshamim8-100-ai-machine-learning-deep-learnin-projects-vs-kennethleungty-failed-ml" tools: ["adilshamim8-100-ai-machine-learning-deep-learnin-projects", "kennethleungty-failed-ml"] --- # 100-AI-Machine-Learning-Deep-Learnin-Projects vs Failed-ML *GraphCanon updated Sep 30, 2026* ## 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. [100-AI-Machine-Learning-Deep-Learnin-Projects](https://adilshamim8.github.io/100-AI-Machine-Learning-Deep-Learnin-Projects/) reports 277 GitHub stars, 23 forks, and 0 open issues, last pushed Sep 21, 2026. [Failed-ML](https://towardsdatascience.com/when-ai-goes-astray-high-profile-machine-learning-mishaps-in-the-real-world-26bd58692195) has 751 stars, 52 forks, and 0 open issues, last pushed Jun 14, 2024. Figures are from public GitHub metadata via [100-AI-Machine-Learning-Deep-Learnin-Projects's repository](https://github.com/AdilShamim8/100-AI-Machine-Learning-Deep-Learnin-Projects) and [Failed-ML's repository](https://github.com/kennethleungty/Failed-ML). | | [100-AI-Machine-Learning-Deep-Learnin-Projects](/tools/adilshamim8-100-ai-machine-learning-deep-learnin-projects.md) | [Failed-ML](/tools/kennethleungty-failed-ml.md) | | --- | --- | --- | | Tagline | A curated, production-grade collection of 100 AI, Machine Learning, and Deep Learning projects | Compilation of high-profile real-world examples of failed machine learning projects | | Stars | 277 | 751 | | Forks | 23 | 52 | | Open issues | 0 | 0 | | Language | HTML | - | | Adopt for | 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 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 | The license for this repository is unknown, which may pose risks for users who need to ensure compliance with specific licensing requirements. | MIT | | Categories | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [100-AI-Machine-Learning-Deep-Learnin-Projects](/tools/adilshamim8-100-ai-machine-learning-deep-learnin-projects.md) | [Failed-ML](/tools/kennethleungty-failed-ml.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 3d | 830d | | Stars delta | +35 (30d) | -2 (30d) | | Full report | [trust report](/tools/adilshamim8-100-ai-machine-learning-deep-learnin-projects/trust.md) | [trust report](/tools/kennethleungty-failed-ml/trust.md) | ## Decision facts: 100-AI-Machine-Learning-Deep-Learnin-Projects - **Requirements:** The repository is primarily focused on Python-based projects, so proficiency in Python is recommended. - **Adopt for:** 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 - **License detail:** The license for this repository is unknown, which may pose risks for users who need to ensure compliance with specific licensing requirements. ## 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 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. ### 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 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 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 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](/tools/adilshamim8-100-ai-machine-learning-deep-learnin-projects/alternatives) and [Failed-ML alternatives](/tools/kennethleungty-failed-ml/alternatives) ([100-AI-Machine-Learning-Deep-Learnin-Projects markdown twin](/tools/adilshamim8-100-ai-machine-learning-deep-learnin-projects/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/adilshamim8-100-ai-machine-learning-deep-learnin-projects-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, 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](/tools/adilshamim8-100-ai-machine-learning-deep-learnin-projects/trust); [Failed-ML trust report](/tools/kennethleungty-failed-ml/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=adilshamim8-100-ai-machine-learning-deep-learnin-projects`](/api/graphcanon/graph?tool=adilshamim8-100-ai-machine-learning-deep-learnin-projects) - 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/_