--- title: "machine-learning-systems-design vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/chiphuyen-machine-learning-systems-design-vs-tensorchord-awesome-llmops" tools: ["chiphuyen-machine-learning-systems-design", "tensorchord-awesome-llmops"] --- # machine-learning-systems-design vs Awesome-LLMOps *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-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. [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-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 1.1k forks, and 317 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [machine-learning-systems-design's repository](https://github.com/chiphuyen/machine-learning-systems-design) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [machine-learning-systems-design](/tools/chiphuyen-machine-learning-systems-design.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | A booklet on machine learning systems design with exercises | An awesome & curated list of best LLMOps tools for developers | | Stars | 10,601 | 5,941 | | Forks | 1,635 | 1,058 | | Open issues | 11 | 317 | | Language | HTML | Shell | | Adopt for | A booklet designed to provide an overview of machine learning systems design, featuring hands-on exercises and practical resources. | Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. | | Persona | developer harness | - | | Runtime | - | - | | License | License information is unavailable. | CC0-1.0 | | Categories | Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [machine-learning-systems-design](/tools/chiphuyen-machine-learning-systems-design.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1253d | 121d | | Open issues (now) | 11 | 317 | | Stars delta | +92 (30d) | +26 (30d) | | Open issues delta | 0 (30d) | +70 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/chiphuyen-machine-learning-systems-design/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/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-LLMOps - **Adopt for:** Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. ## Choose when ### Choose machine-learning-systems-design if… - machine-learning-systems-design is primarily HTML; Awesome-LLMOps is Shell. - 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. - Also covers Developer Tools. - Use for a quick initial introduction to the key aspects of ML system design if you are unfamiliar with the foundational concepts. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; machine-learning-systems-design is HTML. - Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops. - Also covers Computer Vision, LLM Frameworks, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## 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-LLMOps - - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ## Common questions ### What is the difference between machine-learning-systems-design and Awesome-LLMOps? machine-learning-systems-design: A booklet on machine learning systems design with exercises. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories. ### When should I choose machine-learning-systems-design over Awesome-LLMOps? Choose machine-learning-systems-design over Awesome-LLMOps when machine-learning-systems-design is primarily HTML; Awesome-LLMOps is Shell; 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; Also covers Developer Tools; 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-LLMOps over machine-learning-systems-design? Choose Awesome-LLMOps over machine-learning-systems-design when Awesome-LLMOps is primarily Shell; machine-learning-systems-design is HTML; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops; Also covers Computer Vision, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### 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-LLMOps? - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ### Is machine-learning-systems-design or Awesome-LLMOps more popular on GitHub? machine-learning-systems-design has more GitHub stars (10,601 vs 5,941). Stars measure visibility, not whether either tool fits your constraints. ### Are machine-learning-systems-design and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to machine-learning-systems-design or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [machine-learning-systems-design alternatives](/tools/chiphuyen-machine-learning-systems-design/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([machine-learning-systems-design markdown twin](/tools/chiphuyen-machine-learning-systems-design/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/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-tensorchord-awesome-llmops.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-LLMOps? machine-learning-systems-design: Dormant. Awesome-LLMOps: 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 machine-learning-systems-design and Awesome-LLMOps? 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-LLMOps trust report](/tools/tensorchord-awesome-llmops/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/_