--- title: "machine-learning-systems-design vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/chiphuyen-machine-learning-systems-design-vs-wangrongsheng-awesome-llm-resources" tools: ["chiphuyen-machine-learning-systems-design", "wangrongsheng-awesome-llm-resources"] --- # machine-learning-systems-design vs awesome-LLM-resources *GraphCanon updated Sep 20, 2026* ## Verdict Pick machine-learning-systems-design if a booklet designed to provide an overview of machine learning systems design, featuring hands-on exercises and practical resources; pick awesome-LLM-resources if awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. [machine-learning-systems-design](https://huyenchip.com/machine-learning-systems-design/toc.html) reports 11k GitHub stars, 1.6k forks, and 11 open issues, last pushed Apr 15, 2023. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 9.0k stars, 993 forks, and 40 open issues, last pushed Sep 14, 2026. Figures are from public GitHub metadata via [machine-learning-systems-design's repository](https://github.com/chiphuyen/machine-learning-systems-design) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [machine-learning-systems-design](/tools/chiphuyen-machine-learning-systems-design.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | A booklet on machine learning systems design with exercises | Summary of the world's best LLM resources. | | Stars | 10,601 | 8,968 | | Forks | 1,635 | 993 | | Open issues | 11 | 40 | | Language | HTML | - | | Adopt for | A booklet designed to provide an overview of machine learning systems design, featuring hands-on exercises and practical resources. | awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. | | Persona | developer harness | - | | Runtime | - | - | | License | License information is unavailable. | The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. | | Categories | Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [machine-learning-systems-design](/tools/chiphuyen-machine-learning-systems-design.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1253d | 3d | | Open issues (now) | 11 | 40 | | Stars delta | +92 (30d) | +123 (30d) | | Open issues delta | 0 (30d) | +17 (30d) | | Full report | [trust report](/tools/chiphuyen-machine-learning-systems-design/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: machine-learning-systems-design - **Pricing:** freemium - Free to use, no charge for the booklet but additional content like answers to practice questions may be contained in a book that entails a cost. - **Adopt for:** A booklet designed to provide an overview of machine learning systems design, featuring hands-on exercises and practical resources. - **License detail:** License information is unavailable. - **Persona:** developer harness ## Decision facts: awesome-LLM-resources - **Pricing:** freemium - The repository itself is free to use, but some linked resources may require payment or have associated costs. - **Requirements:** The repository does not specify any technical requirements for accessing its content. - **Adopt for:** awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. - **License detail:** The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. ## Choose when ### Choose machine-learning-systems-design if… - Pricing: Free to use, no charge for the booklet but additional content like answers to practice questions may be contained in a book that entails a cost.. - Tags unique to machine-learning-systems-design: data-science, machine-learning-production, mlops. - Use for a quick initial introduction to the key aspects of ML system design if you are unfamiliar with the foundational concepts. ### Choose awesome-LLM-resources if… - Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.. - Requirements: The repository does not specify any technical requirements for accessing its content.. - Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models. - Also covers AI Agents, Computer Vision, LLM Frameworks. - When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ## When NOT to use machine-learning-systems-design - Not recommended if you require an exhaustive guide; this booklet has been superseded by a more comprehensive book 'Designing Machine Learning Systems'. - Avoid using solely as the basis for designing production-ready machine learning systems without further reading and validation from current industry standards or more recent resources. ## When NOT to use awesome-LLM-resources - If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. - When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ## Common questions ### What is the difference between machine-learning-systems-design and awesome-LLM-resources? machine-learning-systems-design: A booklet on machine learning systems design with exercises. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose machine-learning-systems-design over awesome-LLM-resources? Choose machine-learning-systems-design over awesome-LLM-resources when Pricing: Free to use, no charge for the booklet but additional content like answers to practice questions may be contained in a book that entails a cost.; Tags unique to machine-learning-systems-design: data-science, machine-learning-production, mlops; Use for a quick initial introduction to the key aspects of ML system design if you are unfamiliar with the foundational concepts. ### When should I choose awesome-LLM-resources over machine-learning-systems-design? Choose awesome-LLM-resources over machine-learning-systems-design when Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models; Also covers AI Agents, Computer Vision, LLM Frameworks; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ### When should I avoid machine-learning-systems-design? Not recommended if you require an exhaustive guide; this booklet has been superseded by a more comprehensive book 'Designing Machine Learning Systems'. Avoid using solely as the basis for designing production-ready machine learning systems without further reading and validation from current industry standards or more recent resources. ### When should I avoid awesome-LLM-resources? If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ### Is machine-learning-systems-design or awesome-LLM-resources more popular on GitHub? machine-learning-systems-design has more GitHub stars (10,601 vs 8,968). Stars measure visibility, not whether either tool fits your constraints. ### Are machine-learning-systems-design and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to machine-learning-systems-design or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [machine-learning-systems-design alternatives](/tools/chiphuyen-machine-learning-systems-design/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([machine-learning-systems-design markdown twin](/tools/chiphuyen-machine-learning-systems-design/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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/chiphuyen-machine-learning-systems-design-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, machine-learning-systems-design or awesome-LLM-resources? machine-learning-systems-design: Dormant. awesome-LLM-resources: 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 machine-learning-systems-design and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [machine-learning-systems-design trust report](/tools/chiphuyen-machine-learning-systems-design/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=chiphuyen-machine-learning-systems-design`](/api/graphcanon/graph?tool=chiphuyen-machine-learning-systems-design) - 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/_