Comparison
machine-learning-systems-design vs awesome-mlops
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-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
Markdown twin · machine-learning-systems-design alternatives · awesome-mlops alternatives
GraphCanon updated Sep 4, 2026
10views this month
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Trust & integrity
| Signal | machine-learning-systems-design | awesome-mlops |
|---|---|---|
| Maintenance | Dormant (1217d since push) As of Aug 14, 2026 · github_public_v1 | Active (18d since push) As of Sep 4, 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 4, 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-mlops
- A curated list of awesome MLOps tools.
Stars
- machine-learning-systems-design
- 11k
- awesome-mlops
- 5.3k
Forks
- machine-learning-systems-design
- 1.6k
- awesome-mlops
- 775
Open issues
- machine-learning-systems-design
- 11
- awesome-mlops
- 82
Language
- machine-learning-systems-design
- HTML
- awesome-mlops
- Python
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-mlops
- Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
Persona
- machine-learning-systems-design
- developer harness
- awesome-mlops
- -
Runtime
- machine-learning-systems-design
- -
- awesome-mlops
- -
License
- machine-learning-systems-design
- License information is unavailable.
- awesome-mlops
- -
Last pushed
- machine-learning-systems-design
- Apr 15, 2023
- awesome-mlops
- Aug 17, 2026
Categories
- machine-learning-systems-design
- Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
- awesome-mlops
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Maintenance
- machine-learning-systems-design
- Dormant (18%)
- awesome-mlops
- Active (82%)
Days since push
- machine-learning-systems-design
- 1217d
- awesome-mlops
- 18d
Open issues (now)
- machine-learning-systems-design
- 11
- awesome-mlops
- 82
Stars delta
- machine-learning-systems-design
- +54 (30d)
- awesome-mlops
- +36 (30d)
Open issues delta
- machine-learning-systems-design
- 0 (30d)
- awesome-mlops
- +11 (30d)
deps.dev advisories
- machine-learning-systems-design
- No lockfile (source not queried)
- awesome-mlops
- Not queried
OpenSSF Scorecard
- machine-learning-systems-design
- No public record from this source
- awesome-mlops
- Not queried
Full report
- machine-learning-systems-design
- Trust report
- awesome-mlops
- Trust report
Choose machine-learning-systems-design if…
- machine-learning-systems-design is primarily HTML; awesome-mlops is Python.
- 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: machine-learning-production.
- 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-mlops if…
- awesome-mlops is primarily Python; machine-learning-systems-design is HTML.
- Tags unique to awesome-mlops: ai, awesome, machine-learning, machine-learning-engineering.
- You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
When NOT to use awesome-mlops
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform.
- Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
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 (kelvins/awesome-mlops) · observed Sep 4, 2026
- GitHub forks (kelvins/awesome-mlops) · observed Sep 4, 2026
- Last push (kelvins/awesome-mlops) · observed Aug 17, 2026
- License file (unknown) · observed Sep 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: machine-learning-systems-design 11k · awesome-mlops 5.3k (synced Aug 14, 2026).
Common questions
- What is the difference between machine-learning-systems-design and awesome-mlops?
- machine-learning-systems-design: A booklet on machine learning systems design with exercises. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories.
- When should I choose machine-learning-systems-design over awesome-mlops?
- Choose machine-learning-systems-design over awesome-mlops when machine-learning-systems-design is primarily HTML; awesome-mlops is Python; 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: machine-learning-production; 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-mlops over machine-learning-systems-design?
- Choose awesome-mlops over machine-learning-systems-design when awesome-mlops is primarily Python; machine-learning-systems-design is HTML; Tags unique to awesome-mlops: ai, awesome, machine-learning, machine-learning-engineering; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
- 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-mlops?
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
- Is machine-learning-systems-design or awesome-mlops more popular on GitHub?
- machine-learning-systems-design has more GitHub stars (10,509 vs 5,265). Stars measure visibility, not whether either tool fits your constraints.
- Are machine-learning-systems-design and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to machine-learning-systems-design or awesome-mlops?
- GraphCanon lists graph-backed alternatives at machine-learning-systems-design alternatives and awesome-mlops alternatives (machine-learning-systems-design markdown twin, awesome-mlops 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-mlops?
- machine-learning-systems-design: Dormant. awesome-mlops: 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-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: machine-learning-systems-design trust report; awesome-mlops trust report.