--- title: "ml-surveys vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eugeneyan-ml-surveys-vs-rohitg00-ai-engineering-from-scratch" tools: ["eugeneyan-ml-surveys", "rohitg00-ai-engineering-from-scratch"] --- # ml-surveys vs ai-engineering-from-scratch *GraphCanon updated Jul 12, 2026* ## Verdict Pick ml-surveys when tags unique to ml-surveys: embeddings, nlp, recommender-system, reinforcement-learning; pick ai-engineering-from-scratch when pricing: The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up. [ml-surveys](https://github.com/eugeneyan/ml-surveys) reports 2.9k GitHub stars, 291 forks, and 2 open issues, last pushed Mar 17, 2023. [ai-engineering-from-scratch](https://aiengineeringfromscratch.com) has 38k stars, 6.3k forks, and 96 open issues, last pushed Jun 25, 2026. Figures are from public GitHub metadata via [ml-surveys's repository](https://github.com/eugeneyan/ml-surveys) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | 📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc. | Learn it. Build it. Ship it for others. | | Stars | 2,900 | 37,922 | | Forks | 291 | 6,329 | | Open issues | 2 | 96 | | Language | - | Python | | Adopt for | - | Specifically designed for individuals looking to build a comprehensive understanding of AI tools and frameworks from the ground up. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Computer Vision, Vector Databases | AI Agents, Computer Vision, Developer Tools, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 1212d | 15d | | Open issues (now) | 2 | 96 | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/eugeneyan-ml-surveys/trust.md) | [trust report](/tools/rohitg00-ai-engineering-from-scratch/trust.md) | ## Decision facts: ai-engineering-from-scratch - **Pricing:** freemium - The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up - **Adopt for:** Specifically designed for individuals looking to build a comprehensive understanding of AI tools and frameworks from the ground up. ## Choose when ### Choose ml-surveys if… - Tags unique to ml-surveys: embeddings, nlp, recommender-system, reinforcement-learning. - Also covers Vector Databases. - Leaner open-issue backlog (2). ### Choose ai-engineering-from-scratch if… - Pricing: The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up. - Tags unique to ai-engineering-from-scratch: agents, ai-engineering, from-scratch, generative-ai. - Also covers AI Agents, Developer Tools, LLM Frameworks. - When you want to start with foundational knowledge and learn the intricacies behind AI systems. ## When NOT to use ml-surveys - Last GitHub push was 1213 days ago (dormant maintenance, Mar 17, 2023). Validate activity before betting a new project on ml-surveys. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use ai-engineering-from-scratch - If you are looking for a quick setup or ready-to-go solution without diving into the foundational understanding. - When your project requires immediate practical application with less emphasis on self-implemented solutions from scratch. ## Common questions ### What is the difference between ml-surveys and ai-engineering-from-scratch? ml-surveys: 📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.. ai-engineering-from-scratch: Learn it. Build it. Ship it for others.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ml-surveys over ai-engineering-from-scratch? Choose ml-surveys over ai-engineering-from-scratch when Tags unique to ml-surveys: embeddings, nlp, recommender-system, reinforcement-learning; Also covers Vector Databases; Leaner open-issue backlog (2). ### When should I choose ai-engineering-from-scratch over ml-surveys? Choose ai-engineering-from-scratch over ml-surveys when Pricing: The `ai-engineering-from-scratch` repository is free and open-source under an MIT license, but for full access to additional resources or support, a paid option may be provided. Consult official or up; Tags unique to ai-engineering-from-scratch: agents, ai-engineering, from-scratch, generative-ai; Also covers AI Agents, Developer Tools, LLM Frameworks; When you want to start with foundational knowledge and learn the intricacies behind AI systems. ### When should I avoid ml-surveys? Last GitHub push was 1213 days ago (dormant maintenance, Mar 17, 2023). Validate activity before betting a new project on ml-surveys. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid ai-engineering-from-scratch? If you are looking for a quick setup or ready-to-go solution without diving into the foundational understanding. When your project requires immediate practical application with less emphasis on self-implemented solutions from scratch. ### Is ml-surveys or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (37,922 vs 2,900). Stars measure visibility, not whether either tool fits your constraints. ### Are ml-surveys and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (ml-surveys: MIT, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to ml-surveys or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [ml-surveys alternatives](/tools/eugeneyan-ml-surveys/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([ml-surveys markdown twin](/tools/eugeneyan-ml-surveys/alternatives.md), [ai-engineering-from-scratch markdown twin](/tools/rohitg00-ai-engineering-from-scratch/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/eugeneyan-ml-surveys-vs-rohitg00-ai-engineering-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ml-surveys or ai-engineering-from-scratch? ml-surveys: Dormant. ai-engineering-from-scratch: 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 ml-surveys and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ml-surveys trust report](/tools/eugeneyan-ml-surveys/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eugeneyan-ml-surveys`](/api/graphcanon/graph?tool=eugeneyan-ml-surveys) - 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/_