--- title: "ai-engineering-from-scratch vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rohitg00-ai-engineering-from-scratch-vs-windmaple-awesome-automl" tools: ["rohitg00-ai-engineering-from-scratch", "windmaple-awesome-automl"] --- # ai-engineering-from-scratch vs awesome-AutoML *GraphCanon updated Oct 4, 2026* ## Verdict 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; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [ai-engineering-from-scratch](https://aiengineeringfromscratch.com) reports 55k GitHub stars, 9.6k forks, and 114 open issues, last pushed Sep 7, 2026. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 944 stars, 157 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | Learn, build, and deploy AI engineering skills from scratch. | Curating AutoML research and resources | | Stars | 54,935 | 944 | | Forks | 9,649 | 157 | | Open issues | 114 | 1 | | Language | Python | - | | 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 | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | MIT | GPL-3.0 | | Categories | AI Agents, Computer Vision, Developer Tools, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 10d | 194d | | Open issues (now) | 114 | 1 | | Stars delta | +8.1k (30d) | +3 (30d) | | Open issues delta | +7 (30d) | 0 (30d) | | Full report | [trust report](/tools/rohitg00-ai-engineering-from-scratch/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## 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 ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose ai-engineering-from-scratch if… - License: ai-engineering-from-scratch is MIT, awesome-AutoML is GPL-3.0. - Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, deep-learning. - Also covers AI Agents, Computer Vision, Developer Tools. - 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. ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, ai-engineering-from-scratch is MIT. - Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search. - When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ## 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. ## When NOT to use awesome-AutoML - If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. - When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool. ## Common questions ### What is the difference between ai-engineering-from-scratch and awesome-AutoML? ai-engineering-from-scratch: Learn, build, and deploy AI engineering skills from scratch.. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-engineering-from-scratch over awesome-AutoML? Choose ai-engineering-from-scratch over awesome-AutoML when License: ai-engineering-from-scratch is MIT, awesome-AutoML is GPL-3.0; Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, deep-learning; Also covers AI Agents, Computer Vision, Developer Tools; 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 choose awesome-AutoML over ai-engineering-from-scratch? Choose awesome-AutoML over ai-engineering-from-scratch when License: awesome-AutoML is GPL-3.0, ai-engineering-from-scratch is MIT; Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ### 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. ### When should I avoid awesome-AutoML? If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool. ### Is ai-engineering-from-scratch or awesome-AutoML more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (54,935 vs 944). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-engineering-from-scratch and awesome-AutoML open source? Yes - both are open-source projects on GitHub (ai-engineering-from-scratch: MIT, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to ai-engineering-from-scratch or awesome-AutoML? GraphCanon lists graph-backed alternatives at [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([ai-engineering-from-scratch markdown twin](/tools/rohitg00-ai-engineering-from-scratch/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/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/rohitg00-ai-engineering-from-scratch-vs-windmaple-awesome-automl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-engineering-from-scratch or awesome-AutoML? ai-engineering-from-scratch: Active. awesome-AutoML: Slowing. 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 ai-engineering-from-scratch and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rohitg00-ai-engineering-from-scratch`](/api/graphcanon/graph?tool=rohitg00-ai-engineering-from-scratch) - 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/_