Comparison
machine-learning-systems-design vs awesome-LLM-resources
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 offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a.
Markdown twin · machine-learning-systems-design alternatives · awesome-LLM-resources alternatives
GraphCanon updated Aug 17, 2026
vs
Trust & integrity
| Signal | machine-learning-systems-design | awesome-LLM-resources |
|---|---|---|
| Maintenance | Dormant (1217d since push) As of Aug 14, 2026 · github_public_v1 | Very active (2d since push) As of Aug 17, 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 Aug 17, 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
- awesome-LLM-resources
- Summary of the world's best LLM resources.
Stars
- machine-learning-systems-design
- 11k
- awesome-LLM-resources
- 8.8k
Forks
- machine-learning-systems-design
- 1.6k
- awesome-LLM-resources
- 950
Open issues
- machine-learning-systems-design
- 11
- awesome-LLM-resources
- 23
Language
- machine-learning-systems-design
- HTML
- awesome-LLM-resources
- -
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.
- awesome-LLM-resources
- awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a
Persona
- machine-learning-systems-design
- developer harness
- awesome-LLM-resources
- -
Runtime
- machine-learning-systems-design
- -
- awesome-LLM-resources
- -
License
- machine-learning-systems-design
- License information is unavailable.
- awesome-LLM-resources
- Apache-2.0
Last pushed
- machine-learning-systems-design
- Apr 15, 2023
- awesome-LLM-resources
- Aug 14, 2026
Categories
- machine-learning-systems-design
- Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
- awesome-LLM-resources
- AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- machine-learning-systems-design
- Dormant (18%)
- awesome-LLM-resources
- Very active (96%)
Days since push
- machine-learning-systems-design
- 1217d
- awesome-LLM-resources
- 2d
Open issues (now)
- machine-learning-systems-design
- 11
- awesome-LLM-resources
- 23
Stars delta
- machine-learning-systems-design
- +54 (30d)
- awesome-LLM-resources
- +142 (30d)
Open issues delta
- machine-learning-systems-design
- 0 (30d)
- awesome-LLM-resources
- -13 (30d)
deps.dev advisories
- machine-learning-systems-design
- No lockfile (source not queried)
- awesome-LLM-resources
- Not queried
OpenSSF Scorecard
- machine-learning-systems-design
- No public record from this source
- awesome-LLM-resources
- Not queried
Full report
- machine-learning-systems-design
- Trust report
- awesome-LLM-resources
- 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.
- 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 awesome-LLM-resources if…
- Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models.
- Also covers AI Agents, LLM Frameworks.
- - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
When NOT to use awesome-LLM-resources
- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage.
- - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
Explore
machine-learning-systems-design trust report →awesome-LLM-resources trust report →Data & Retrieval category →Developer Tools category →Evaluation & Observability category →Inference & Serving category →Model Training category →AI Agents category →LLM Frameworks category →All comparisonsStack workflowsTrending tools
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 (WangRongsheng/awesome-LLM-resources) · observed Aug 17, 2026
- GitHub forks (WangRongsheng/awesome-LLM-resources) · observed Aug 17, 2026
- Last push (WangRongsheng/awesome-LLM-resources) · observed Aug 14, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 10, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: machine-learning-systems-design 11k · awesome-LLM-resources 8.8k (synced Aug 14, 2026).
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; Also covers Data & Retrieval; 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 Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, LLM Frameworks; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
- 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?
- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
- Is machine-learning-systems-design or awesome-LLM-resources more popular on GitHub?
- machine-learning-systems-design has more GitHub stars (10,509 vs 8,845). 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 and awesome-LLM-resources alternatives (machine-learning-systems-design markdown twin, awesome-LLM-resources 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 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; awesome-LLM-resources trust report.