--- title: "Failed-ML vs AI-For-Beginners" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kennethleungty-failed-ml-vs-microsoft-ai-for-beginners" tools: ["kennethleungty-failed-ml", "microsoft-ai-for-beginners"] --- # Failed-ML vs AI-For-Beginners *GraphCanon updated Jul 11, 2026* ## Verdict Pick Failed-ML when tags unique to Failed-ML: classification, data-engineering, data-quality, data-science; pick AI-For-Beginners when tags unique to AI-For-Beginners: cnn, gan, machine learning, microsoft-for-beginners. [Failed-ML](https://towardsdatascience.com/when-ai-goes-astray-high-profile-machine-learning-mishaps-in-the-real-world-26bd58692195) reports 753 GitHub stars, 51 forks, and 0 open issues, last pushed Jun 14, 2024. [AI-For-Beginners](https://github.com/microsoft/AI-For-Beginners) has 52k stars, 11k forks, and 4 open issues, last pushed Jul 8, 2026. Figures are from public GitHub metadata via [Failed-ML's repository](https://github.com/kennethleungty/Failed-ML) and [AI-For-Beginners's repository](https://github.com/microsoft/AI-For-Beginners). | | [Failed-ML](/tools/kennethleungty-failed-ml.md) | [AI-For-Beginners](/tools/microsoft-ai-for-beginners.md) | | --- | --- | --- | | Tagline | Compilation of high-profile real-world examples of failed machine learning projects | 12 Weeks, 24 Lessons, AI for All! | | Stars | 753 | 52,098 | | Forks | 51 | 10,536 | | Open issues | 0 | 4 | | Language | - | Jupyter Notebook | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Computer Vision, LLM Frameworks, Model Training | Computer Vision, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Failed-ML](/tools/kennethleungty-failed-ml.md) | [AI-For-Beginners](/tools/microsoft-ai-for-beginners.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 757d | 2d | | Open issues (now) | 0 | 4 | | Owner type | User | Organization | | Security scan | No lockfile | 3 low (3 low) | | Full report | [trust report](/tools/kennethleungty-failed-ml/trust.md) | [trust report](/tools/microsoft-ai-for-beginners/trust.md) | ## Choose when ### Choose Failed-ML if… - Tags unique to Failed-ML: classification, data-engineering, data-quality, data-science. - Also covers LLM Frameworks. - Leaner open-issue backlog (0). ### Choose AI-For-Beginners if… - Tags unique to AI-For-Beginners: cnn, gan, machine learning, microsoft-for-beginners. - Also covers Vector Databases. - More GitHub stars (52k vs 753) - visibility, not fit. ## When NOT to use Failed-ML - Last GitHub push was 758 days ago (dormant maintenance, Jun 14, 2024). Validate activity before betting a new project on Failed-ML. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use AI-For-Beginners - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between Failed-ML and AI-For-Beginners? Failed-ML: Compilation of high-profile real-world examples of failed machine learning projects. AI-For-Beginners: 12 Weeks, 24 Lessons, AI for All!. See the comparison table for live GitHub stats and shared categories. ### When should I choose Failed-ML over AI-For-Beginners? Choose Failed-ML over AI-For-Beginners when Tags unique to Failed-ML: classification, data-engineering, data-quality, data-science; Also covers LLM Frameworks; Leaner open-issue backlog (0). ### When should I choose AI-For-Beginners over Failed-ML? Choose AI-For-Beginners over Failed-ML when Tags unique to AI-For-Beginners: cnn, gan, machine learning, microsoft-for-beginners; Also covers Vector Databases; More GitHub stars (52k vs 753) - visibility, not fit. ### When should I avoid Failed-ML? Last GitHub push was 758 days ago (dormant maintenance, Jun 14, 2024). Validate activity before betting a new project on Failed-ML. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid AI-For-Beginners? Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is Failed-ML or AI-For-Beginners more popular on GitHub? AI-For-Beginners has more GitHub stars (52,098 vs 753). Stars measure visibility, not whether either tool fits your constraints. ### Are Failed-ML and AI-For-Beginners open source? Yes - both are open-source projects on GitHub (Failed-ML: MIT, AI-For-Beginners: MIT). ### Where can I find alternatives to Failed-ML or AI-For-Beginners? GraphCanon lists graph-backed alternatives at [Failed-ML alternatives](/tools/kennethleungty-failed-ml/alternatives) and [AI-For-Beginners alternatives](/tools/microsoft-ai-for-beginners/alternatives) ([Failed-ML markdown twin](/tools/kennethleungty-failed-ml/alternatives.md), [AI-For-Beginners markdown twin](/tools/microsoft-ai-for-beginners/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/kennethleungty-failed-ml-vs-microsoft-ai-for-beginners.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Failed-ML or AI-For-Beginners? Failed-ML: Dormant. AI-For-Beginners: Very active. 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 Failed-ML and AI-For-Beginners? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Failed-ML trust report](/tools/kennethleungty-failed-ml/trust); [AI-For-Beginners trust report](/tools/microsoft-ai-for-beginners/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kennethleungty-failed-ml`](/api/graphcanon/graph?tool=kennethleungty-failed-ml) - 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/_