Comparison
vega vs awesome-AutoML
Verdict
Pick vega if vega is an AutoML toolchain from Huawei Noah's Ark Lab that streamlines model building and selection with Python; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · vega alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | vega | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (1266d since push) As of 2w · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization 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
- vega
- AutoML tools chain
- awesome-AutoML
- Curating AutoML research and resources
Stars
- vega
- 849
- awesome-AutoML
- 941
Forks
- vega
- 177
- awesome-AutoML
- 156
Open issues
- vega
- 53
- awesome-AutoML
- 1
Language
- vega
- Python
- awesome-AutoML
- -
Adopt for
- vega
- Vega is an AutoML toolchain from Huawei Noah's Ark Lab that streamlines model building and selection with Python.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- vega
- -
- awesome-AutoML
- -
Runtime
- vega
- -
- awesome-AutoML
- -
License
- vega
- Other
- awesome-AutoML
- GPL-3.0
Last pushed
- vega
- Feb 15, 2023
- awesome-AutoML
- Mar 24, 2026
Categories
- vega
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- vega
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- vega
- 1266d
- awesome-AutoML
- 133d
Open issues (now)
- vega
- 53
- awesome-AutoML
- 1
Owner type
- vega
- Organization
- awesome-AutoML
- User
Full report
- vega
- Trust report
- awesome-AutoML
- Trust report
Choose vega if…
- License: vega is Other, awesome-AutoML is GPL-3.0.
- When leveraging the specific optimizations offered by Huawei Noah's Ark Lab in your automated machine learning workflows
When NOT to use vega
- If dependency on proprietary solutions, such as those from a single vendor like Huawei, needs to be avoided
- When you require an extensive open community support or the flexibility traditionally offered by more established open-source AutoML tools
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, vega is Other.
- Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning, neural-architecture-search.
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
When NOT to use awesome-AutoML
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides.
- When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (huawei-noah/vega) · observed Aug 4, 2026
- GitHub forks (huawei-noah/vega) · observed Aug 4, 2026
- Last push (huawei-noah/vega) · observed Feb 15, 2023
- License file (Other) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: vega 849 · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between vega and awesome-AutoML?
- vega: AutoML tools chain. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose vega over awesome-AutoML?
- Choose vega over awesome-AutoML when License: vega is Other, awesome-AutoML is GPL-3.0; When leveraging the specific optimizations offered by Huawei Noah's Ark Lab in your automated machine learning workflows.
- When should I choose awesome-AutoML over vega?
- Choose awesome-AutoML over vega when License: awesome-AutoML is GPL-3.0, vega is Other; Tags unique to awesome-AutoML: hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- When should I avoid vega?
- If dependency on proprietary solutions, such as those from a single vendor like Huawei, needs to be avoided When you require an extensive open community support or the flexibility traditionally offered by more established open-source AutoML tools
- When should I avoid awesome-AutoML?
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
- Is vega or awesome-AutoML more popular on GitHub?
- awesome-AutoML has more GitHub stars (941 vs 849). Stars measure visibility, not whether either tool fits your constraints.
- Are vega and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (vega: Other, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to vega or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at vega alternatives and awesome-AutoML alternatives (vega markdown twin, awesome-AutoML 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, vega or awesome-AutoML?
- vega: Dormant. awesome-AutoML: 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 vega and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: vega trust report; awesome-AutoML trust report.