Comparison
Awesome-AutoDL vs awesome-mlops
Verdict
Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
Markdown twin · Awesome-AutoDL alternatives · awesome-mlops alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-AutoDL | awesome-mlops |
|---|---|---|
| Maintenance | Dormant (1408d since push) As of 2w · github_public_v1 | Slowing (97d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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-AutoDL
- Curated list of automated deep learning resources covering AutoDL, NAS, HPO
- awesome-mlops
- A curated list of awesome MLOps tools.
Stars
- Awesome-AutoDL
- 2.3k
- awesome-mlops
- 5.2k
Forks
- Awesome-AutoDL
- 319
- awesome-mlops
- 762
Open issues
- Awesome-AutoDL
- 2
- awesome-mlops
- 71
Language
- Awesome-AutoDL
- Python
- awesome-mlops
- Python
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- awesome-mlops
- Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
Persona
- Awesome-AutoDL
- -
- awesome-mlops
- -
Runtime
- Awesome-AutoDL
- -
- awesome-mlops
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- awesome-mlops
- -
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- awesome-mlops
- Apr 29, 2026
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- awesome-mlops
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Maintenance
- Awesome-AutoDL
- Dormant (18%)
- awesome-mlops
- Slowing (36%)
Days since push
- Awesome-AutoDL
- 1408d
- awesome-mlops
- 97d
Open issues (now)
- Awesome-AutoDL
- 2
- awesome-mlops
- 71
Full report
- Awesome-AutoDL
- Trust report
- awesome-mlops
- Trust report
Choose Awesome-AutoDL if…
- Tags unique to Awesome-AutoDL: autodl, automl, deep-learning, hyper-parameter-optimization.
- Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS).
- Leaner open-issue backlog (2).
When NOT to use Awesome-AutoDL
- Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned.
- Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide.
Choose awesome-mlops if…
- Tags unique to awesome-mlops: ai, data-science, machine-learning, machine-learning-engineering.
- Also covers Evaluation & Observability, 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (D-X-Y/Awesome-AutoDL) · observed Aug 4, 2026
- GitHub forks (D-X-Y/Awesome-AutoDL) · observed Aug 4, 2026
- Last push (D-X-Y/Awesome-AutoDL) · observed Sep 26, 2022
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (kelvins/awesome-mlops) · observed Aug 4, 2026
- GitHub forks (kelvins/awesome-mlops) · observed Aug 4, 2026
- Last push (kelvins/awesome-mlops) · observed Apr 29, 2026
- License file (unknown) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-AutoDL 2.3k · awesome-mlops 5.2k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-AutoDL and awesome-mlops?
- Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-AutoDL over awesome-mlops?
- Choose Awesome-AutoDL over awesome-mlops when Tags unique to Awesome-AutoDL: autodl, automl, deep-learning, hyper-parameter-optimization; Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS); Leaner open-issue backlog (2).
- When should I choose awesome-mlops over Awesome-AutoDL?
- Choose awesome-mlops over Awesome-AutoDL when Tags unique to awesome-mlops: ai, data-science, machine-learning, machine-learning-engineering; Also covers Evaluation & Observability, Inference & Serving; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
- When should I avoid Awesome-AutoDL?
- Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide.
- 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 Awesome-AutoDL or awesome-mlops more popular on GitHub?
- awesome-mlops has more GitHub stars (5,229 vs 2,339). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-AutoDL and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Awesome-AutoDL or awesome-mlops?
- GraphCanon lists graph-backed alternatives at Awesome-AutoDL alternatives and awesome-mlops alternatives (Awesome-AutoDL 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, Awesome-AutoDL or awesome-mlops?
- Awesome-AutoDL: Dormant. awesome-mlops: 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 Awesome-AutoDL and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-AutoDL trust report; awesome-mlops trust report.