Comparison
machine-learning-systems-design vs Awesome-LLMOps
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.
Markdown twin · machine-learning-systems-design alternatives · Awesome-LLMOps alternatives
GraphCanon updated Aug 20, 2026
10views this month
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Trust & integrity
| Signal | machine-learning-systems-design | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (1217d since push) As of Aug 14, 2026 · github_public_v1 | Slowing (91d since push) As of Aug 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Aug 14, 2026 · github_public_v1 | Not a fork · Organization account As of Aug 20, 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 Jul 11, 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-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- machine-learning-systems-design
- 11k
- Awesome-LLMOps
- 5.9k
Forks
- machine-learning-systems-design
- 1.6k
- Awesome-LLMOps
- 993
Open issues
- machine-learning-systems-design
- 11
- Awesome-LLMOps
- 247
Language
- machine-learning-systems-design
- HTML
- Awesome-LLMOps
- Shell
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-LLMOps
- 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
- machine-learning-systems-design
- developer harness
- Awesome-LLMOps
- -
Runtime
- machine-learning-systems-design
- -
- Awesome-LLMOps
- -
License
- machine-learning-systems-design
- License information is unavailable.
- Awesome-LLMOps
- CC0-1.0
Last pushed
- machine-learning-systems-design
- Apr 15, 2023
- Awesome-LLMOps
- May 21, 2026
Categories
- machine-learning-systems-design
- Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- machine-learning-systems-design
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- machine-learning-systems-design
- 1217d
- Awesome-LLMOps
- 91d
Open issues (now)
- machine-learning-systems-design
- 11
- Awesome-LLMOps
- 247
Stars delta
- machine-learning-systems-design
- +54 (30d)
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- machine-learning-systems-design
- 0 (30d)
- Awesome-LLMOps
- +66 (30d)
Owner type
- machine-learning-systems-design
- User
- Awesome-LLMOps
- Organization
deps.dev advisories
- machine-learning-systems-design
- No lockfile (source not queried)
- Awesome-LLMOps
- Not queried
OpenSSF Scorecard
- machine-learning-systems-design
- No public record from this source
- Awesome-LLMOps
- Not queried
Full report
- machine-learning-systems-design
- Trust report
- Awesome-LLMOps
- Trust report
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.
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-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 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.
Explore
machine-learning-systems-design trust report →Awesome-LLMOps trust report →Data & Retrieval category →Developer Tools category →Evaluation & Observability category →Inference & Serving category →Model Training category →Computer Vision category →LLM Frameworks category →Speech & Audio 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 (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 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 · Awesome-LLMOps 5.9k (synced Aug 14, 2026).
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 (10,509 vs 5,915). 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 and Awesome-LLMOps alternatives (machine-learning-systems-design markdown twin, Awesome-LLMOps 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-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; Awesome-LLMOps trust report.