--- title: "Awesome-AutoDL vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-rohitg00-ai-engineering-from-scratch" tools: ["d-x-y-awesome-autodl", "rohitg00-ai-engineering-from-scratch"] --- # Awesome-AutoDL vs ai-engineering-from-scratch *GraphCanon updated Oct 4, 2026* ## Verdict Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick ai-engineering-from-scratch if ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and. [Awesome-AutoDL](https://github.com/D-X-Y/Awesome-AutoDL) reports 2.3k GitHub stars, 317 forks, and 2 open issues, last pushed Sep 26, 2022. [ai-engineering-from-scratch](https://aiengineeringfromscratch.com) has 55k stars, 9.6k forks, and 114 open issues, last pushed Sep 7, 2026. Figures are from public GitHub metadata via [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | Learn, build, and deploy AI engineering skills from scratch. | | Stars | 2,344 | 54,935 | | Forks | 317 | 9,649 | | Open issues | 2 | 114 | | Language | Python | Python | | Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and | | Persona | - | - | | Runtime | - | - | | License | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | MIT | | Categories | Developer Tools, Model Training | AI Agents, Computer Vision, Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 1469d | 10d | | Open issues (now) | 2 | 114 | | Stars delta | +5 (30d) | +8.1k (30d) | | Open issues delta | 0 (30d) | +7 (30d) | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/rohitg00-ai-engineering-from-scratch/trust.md) | ## Decision facts: Awesome-AutoDL - **Adopt for:** A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. - **License detail:** MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. ## Decision facts: ai-engineering-from-scratch - **Adopt for:** ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and ## Choose when ### Choose Awesome-AutoDL if… - Tags unique to Awesome-AutoDL: autodl, automl, awesome, hyper-parameter-optimization. - Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS). - Leaner open-issue backlog (2). ### Choose ai-engineering-from-scratch if… - Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, from-scratch. - Also covers AI Agents, Computer Vision. - when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning. ## When NOT to use Awesome-AutoDL - Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. - Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide. ## When NOT to use ai-engineering-from-scratch - if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. - when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. - if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. - when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments. ## Common questions ### What is the difference between Awesome-AutoDL and ai-engineering-from-scratch? Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. ai-engineering-from-scratch: Learn, build, and deploy AI engineering skills from scratch.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-AutoDL over ai-engineering-from-scratch? Choose Awesome-AutoDL over ai-engineering-from-scratch when Tags unique to Awesome-AutoDL: autodl, automl, awesome, hyper-parameter-optimization; Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS); Leaner open-issue backlog (2). ### When should I choose ai-engineering-from-scratch over Awesome-AutoDL? Choose ai-engineering-from-scratch over Awesome-AutoDL when Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, from-scratch; Also covers AI Agents, Computer Vision; when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning. ### When should I avoid Awesome-AutoDL? Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide. ### When should I avoid ai-engineering-from-scratch? if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments. ### Is Awesome-AutoDL or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (54,935 vs 2,344). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AutoDL and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to Awesome-AutoDL or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/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/d-x-y-awesome-autodl-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, Awesome-AutoDL or ai-engineering-from-scratch? Awesome-AutoDL: 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 Awesome-AutoDL and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=d-x-y-awesome-autodl`](/api/graphcanon/graph?tool=d-x-y-awesome-autodl) - 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/_