Comparison
awesome-mlops vs awesome-list-of-awesomes
Verdict
Pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML; pick awesome-list-of-awesomes if a directory of curated 'awesome lists' on AI topics like ML, DL, CV.
Markdown twin · awesome-mlops alternatives · awesome-list-of-awesomes alternatives
GraphCanon updated Oct 4, 2026
8views this month
vs
Trust & integrity
| Signal | awesome-mlops | awesome-list-of-awesomes |
|---|---|---|
| Maintenance | Steady (48d since push) As of Oct 4, 2026 · github_public_v1 | Dormant (1045d since push) As of Sep 24, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 24, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- awesome-mlops
- A curated list of awesome MLOps tools.
- awesome-list-of-awesomes
- A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research
Stars
- awesome-mlops
- 5.3k
- awesome-list-of-awesomes
- 345
Forks
- awesome-mlops
- 792
- awesome-list-of-awesomes
- 48
Open issues
- awesome-mlops
- 98
- awesome-list-of-awesomes
- 1
Language
- awesome-mlops
- Python
- awesome-list-of-awesomes
- -
Adopt for
- awesome-mlops
- Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
- awesome-list-of-awesomes
- A directory of curated 'awesome lists' on AI topics like ML, DL, CV.
Persona
- awesome-mlops
- -
- awesome-list-of-awesomes
- -
Runtime
- awesome-mlops
- -
- awesome-list-of-awesomes
- -
License
- awesome-mlops
- -
- awesome-list-of-awesomes
- MIT
Last pushed
- awesome-mlops
- Aug 17, 2026
- awesome-list-of-awesomes
- Nov 13, 2023
Categories
- awesome-mlops
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
- awesome-list-of-awesomes
- Computer Vision, Evaluation & Observability, Model Training
Trust and health
Maintenance
- awesome-mlops
- Steady (60%)
- awesome-list-of-awesomes
- Dormant (18%)
Days since push
- awesome-mlops
- 48d
- awesome-list-of-awesomes
- 1045d
Open issues (now)
- awesome-mlops
- 98
- awesome-list-of-awesomes
- 1
Stars delta
- awesome-mlops
- +55 (30d)
- awesome-list-of-awesomes
- 0 (30d)
Open issues delta
- awesome-mlops
- +27 (30d)
- awesome-list-of-awesomes
- 0 (30d)
Full report
- awesome-mlops
- Trust report
- awesome-list-of-awesomes
- Trust report
Choose awesome-mlops if…
- Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml.
- Also covers Developer Tools, Inference & Serving.
- 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.
Choose awesome-list-of-awesomes if…
- Tags unique to awesome-list-of-awesomes: computer-vision, deep-learning, natural language processing.
- Also covers Computer Vision.
- When you need diverse resources covering specific areas in data science and machine learning
When NOT to use awesome-list-of-awesomes
- If you require the latest updates, as not all linked lists are actively maintained
- For deeply curated content on new or niche topics not covered
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (kelvins/awesome-mlops) · observed Oct 4, 2026
- GitHub forks (kelvins/awesome-mlops) · observed Oct 4, 2026
- Last push (kelvins/awesome-mlops) · observed Aug 17, 2026
- License file (unknown) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Nachimak28/awesome-list-of-awesomes) · observed Sep 24, 2026
- GitHub forks (Nachimak28/awesome-list-of-awesomes) · observed Sep 24, 2026
- Last push (Nachimak28/awesome-list-of-awesomes) · observed Nov 13, 2023
- License file (MIT) · observed Sep 24, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-mlops 5.3k · awesome-list-of-awesomes 345 (synced Oct 4, 2026).
Common questions
- What is the difference between awesome-mlops and awesome-list-of-awesomes?
- awesome-mlops: A curated list of awesome MLOps tools.. awesome-list-of-awesomes: A curated list of 'Awesome' topic lists related to data lifecycle, ML and DL research. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-mlops over awesome-list-of-awesomes?
- Choose awesome-mlops over awesome-list-of-awesomes when Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml; Also covers Developer Tools, Inference & Serving; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
- When should I choose awesome-list-of-awesomes over awesome-mlops?
- Choose awesome-list-of-awesomes over awesome-mlops when Tags unique to awesome-list-of-awesomes: computer-vision, deep-learning, natural language processing; Also covers Computer Vision; When you need diverse resources covering specific areas in data science and machine learning.
- 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.
- When should I avoid awesome-list-of-awesomes?
- If you require the latest updates, as not all linked lists are actively maintained For deeply curated content on new or niche topics not covered
- Is awesome-mlops or awesome-list-of-awesomes more popular on GitHub?
- awesome-mlops has more GitHub stars (5,284 vs 345). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-mlops and awesome-list-of-awesomes open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-mlops or awesome-list-of-awesomes?
- GraphCanon lists graph-backed alternatives at awesome-mlops alternatives and awesome-list-of-awesomes alternatives (awesome-mlops markdown twin, awesome-list-of-awesomes 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, awesome-mlops or awesome-list-of-awesomes?
- awesome-mlops: Steady. awesome-list-of-awesomes: Dormant. 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 awesome-mlops and awesome-list-of-awesomes?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-mlops trust report; awesome-list-of-awesomes trust report.