Comparison
machine-learning-systems-design vs llm-course
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 llm-course if 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.
Markdown twin · machine-learning-systems-design alternatives · llm-course alternatives
GraphCanon updated Sep 7, 2026
vs
Trust & integrity
| Signal | machine-learning-systems-design | llm-course |
|---|---|---|
| Maintenance | Dormant (1217d since push) As of Aug 14, 2026 · github_public_v1 | Slowing (214d since push) As of Sep 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Aug 14, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 7, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 |
| deps.dev advisories | No lockfile (source not queried) As of Aug 16, 2026 · deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | No public record from this source As of Aug 2, 2026 · openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- machine-learning-systems-design
- A booklet on machine learning systems design with exercises
- llm-course
- Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
Stars
- machine-learning-systems-design
- 11k
- llm-course
- 82k
Forks
- machine-learning-systems-design
- 1.6k
- llm-course
- 9.6k
Open issues
- machine-learning-systems-design
- 11
- llm-course
- 91
Language
- machine-learning-systems-design
- HTML
- llm-course
- -
Adopt for
- machine-learning-systems-design
- A booklet designed to provide an overview of machine learning systems design, featuring hands-on exercises and practical resources.
- llm-course
- 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
- machine-learning-systems-design
- developer harness
- llm-course
- -
Runtime
- machine-learning-systems-design
- -
- llm-course
- -
License
- machine-learning-systems-design
- License information is unavailable.
- llm-course
- Apache-2.0
Last pushed
- machine-learning-systems-design
- Apr 15, 2023
- llm-course
- Feb 5, 2026
Categories
- machine-learning-systems-design
- Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
- llm-course
- Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- machine-learning-systems-design
- Dormant (18%)
- llm-course
- Slowing (36%)
Days since push
- machine-learning-systems-design
- 1217d
- llm-course
- 214d
Open issues (now)
- machine-learning-systems-design
- 11
- llm-course
- 91
Stars delta
- machine-learning-systems-design
- +54 (30d)
- llm-course
- +863 (30d)
Open issues delta
- machine-learning-systems-design
- 0 (30d)
- llm-course
- +5 (30d)
deps.dev advisories
- machine-learning-systems-design
- No lockfile (source not queried)
- llm-course
- Not queried
OpenSSF Scorecard
- machine-learning-systems-design
- No public record from this source
- llm-course
- Not queried
Full report
- machine-learning-systems-design
- Trust report
- llm-course
- Trust report
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.
- Also covers Data & Retrieval, 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 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.
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 LLM Frameworks.
- - When you want a comprehensive roadmap for understanding large language models including fundamental 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (chiphuyen/machine-learning-systems-design) · observed Aug 14, 2026
- GitHub forks (chiphuyen/machine-learning-systems-design) · observed Aug 14, 2026
- Last push (chiphuyen/machine-learning-systems-design) · observed Apr 15, 2023
- License file (unknown) · observed Aug 14, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (mlabonne/llm-course) · observed Sep 7, 2026
- GitHub forks (mlabonne/llm-course) · observed Sep 7, 2026
- Last push (mlabonne/llm-course) · observed Feb 5, 2026
- License file (Apache-2.0) · observed Sep 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: machine-learning-systems-design 11k · llm-course 82k (synced Aug 14, 2026).
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. 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 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; Also covers Data & Retrieval, 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 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 LLM Frameworks; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge.
- 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 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 (82,375 vs 10,509). 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 and llm-course alternatives (machine-learning-systems-design markdown twin, llm-course markdown twin), 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 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; llm-course trust report.