--- title: "machine-learning-systems-design vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/chiphuyen-machine-learning-systems-design-vs-mlabonne-llm-course" tools: ["chiphuyen-machine-learning-systems-design", "mlabonne-llm-course"] --- # machine-learning-systems-design vs llm-course *GraphCanon updated Jul 15, 2026* ## Verdict Pick machine-learning-systems-design when tags unique to machine-learning-systems-design: data-science, html, machine-learning-production, mlops; pick llm-course when requirements: Course materials are available in Colab notebooks; access requires a Google account. [machine-learning-systems-design](https://huyenchip.com/machine-learning-systems-design/toc.html) reports 10k GitHub stars, 1.6k forks, and 11 open issues, last pushed Apr 15, 2023. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 85 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [machine-learning-systems-design's repository](https://github.com/chiphuyen/machine-learning-systems-design) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [machine-learning-systems-design](/tools/chiphuyen-machine-learning-systems-design.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems", which is `dmls-book` | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 10,455 | 80,904 | | Forks | 1,616 | 9,424 | | Open issues | 11 | 85 | | Language | HTML | - | | Adopt for | - | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Data & Retrieval, Inference & Serving, Model Training | 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) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1186d | 159d | | Open issues (now) | 11 | 85 | | Full report | [trust report](/tools/chiphuyen-machine-learning-systems-design/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Decision facts: llm-course - **Requirements:** Course materials are available in Colab notebooks; access requires a Google account - **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to - **License detail:** Apache-2.0 ## Choose when ### Choose machine-learning-systems-design if… - Tags unique to machine-learning-systems-design: data-science, html, machine-learning-production, mlops. - Also covers Data & Retrieval. - Leaner open-issue backlog (11). ### Choose llm-course if… - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, course, large-language-models, machine-learning. - Also covers Evaluation & Observability, LLM Frameworks. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## When NOT to use machine-learning-systems-design - Last GitHub push was 1187 days ago (dormant maintenance, Apr 15, 2023). Validate activity before betting a new project on machine-learning-systems-design. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use llm-course - - If you only require a quick introduction to LLMs without deep dive into core components - - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ## Common questions ### What is the difference between machine-learning-systems-design and llm-course? machine-learning-systems-design: A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems", which is `dmls-book`. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose machine-learning-systems-design over llm-course? Choose machine-learning-systems-design over llm-course when Tags unique to machine-learning-systems-design: data-science, html, machine-learning-production, mlops; Also covers Data & Retrieval; Leaner open-issue backlog (11). ### When should I choose llm-course over machine-learning-systems-design? Choose llm-course over machine-learning-systems-design when Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, course, large-language-models, machine-learning; Also covers Evaluation & Observability, LLM Frameworks; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I avoid machine-learning-systems-design? Last GitHub push was 1187 days ago (dormant maintenance, Apr 15, 2023). Validate activity before betting a new project on machine-learning-systems-design. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid llm-course? - If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ### Is machine-learning-systems-design or llm-course more popular on GitHub? llm-course has more GitHub stars (80,904 vs 10,455). Stars measure visibility, not whether either tool fits your constraints. ### Are machine-learning-systems-design and llm-course open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to machine-learning-systems-design or llm-course? GraphCanon lists graph-backed alternatives at [machine-learning-systems-design alternatives](/tools/chiphuyen-machine-learning-systems-design/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([machine-learning-systems-design markdown twin](/tools/chiphuyen-machine-learning-systems-design/alternatives.md), [llm-course markdown twin](/tools/mlabonne-llm-course/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-mlabonne-llm-course.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 llm-course? machine-learning-systems-design: Dormant. llm-course: 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 llm-course? 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); [llm-course trust report](/tools/mlabonne-llm-course/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/_