Failed-ML
Compilation of high-profile real-world examples of failed machine learning projects
GraphCanon updated Sep 23, 2026 · GitHub synced Sep 23, 2026
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Decision brief
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.
Good fit when
- When you seek specific historical examples of where machine learning application went wrong, aiding in understanding the potential mistakes and challenges one might face.
- For educating teams or stakeholders on why certain ML projects failed, providing real-world context to abstract concepts such as data bias, model validation issues, and ethical concerns.
Avoid when
- 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.
- 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.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (830d since push)
- As of Sep 23, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 23, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/kennethleungty/Failed-MLSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides insights into the failures of various ML projects across different domains to aid in understanding common pitfalls and mistakes in implementing machine learning solutions.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Tags
README
Failed Machine Learning (FML) High profile real world examples of failed machine learning projects <hr <p align="center" <img src="https://github.com/kennethleungty/Failed ML/blob/main/images/fml.gif?raw=true" </p “Success is not final, failure is not fatal. It is the courage to...
For agents
This page has a .md twin and JSON over the API.