Comparison
scikit-learn vs awesome-AutoML
Verdict
Pick scikit-learn if use scikit-learn for Python-based machine learning tasks that require robust algorithms, comprehensive documentation, and extensive community support; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · scikit-learn alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | scikit-learn | awesome-AutoML |
|---|---|---|
| Maintenance | Very active (1d since push) As of 3w · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · 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
- scikit-learn
- machine learning in Python
- awesome-AutoML
- Curating AutoML research and resources
Stars
- scikit-learn
- 67k
- awesome-AutoML
- 941
Forks
- scikit-learn
- 27k
- awesome-AutoML
- 156
Open issues
- scikit-learn
- 2.1k
- awesome-AutoML
- 1
Language
- scikit-learn
- Python
- awesome-AutoML
- -
Adopt for
- scikit-learn
- Use scikit-learn for Python-based machine learning tasks that require robust algorithms, comprehensive documentation, and extensive community support.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- scikit-learn
- -
- awesome-AutoML
- -
Runtime
- scikit-learn
- -
- awesome-AutoML
- -
License
- scikit-learn
- BSD-3-Clause
- awesome-AutoML
- GPL-3.0
Last pushed
- scikit-learn
- Aug 1, 2026
- awesome-AutoML
- Mar 24, 2026
Categories
- scikit-learn
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- scikit-learn
- Very active (96%)
- awesome-AutoML
- Slowing (36%)
Days since push
- scikit-learn
- 1d
- awesome-AutoML
- 133d
Open issues (now)
- scikit-learn
- 2.1k
- awesome-AutoML
- 1
Owner type
- scikit-learn
- Organization
- awesome-AutoML
- User
Full report
- scikit-learn
- Trust report
- awesome-AutoML
- Trust report
Choose scikit-learn if…
- License: scikit-learn is BSD-3-Clause, awesome-AutoML is GPL-3.0.
- Tags unique to scikit-learn: data-analysis, data-science, machine-learning, python.
- When you need a well-documented library with clear examples and strong community support.
When NOT to use scikit-learn
- Avoid if you require cutting-edge deep learning capabilities or model training that is more efficiently managed with GPU accelerators.
- Not ideal when dealing with very large datasets that benefit from out-of-core computation, as it lacks native support for such functionalities.
- If real-time machine learning predictions are critical and need ultra-low latency, other tools might offer better performance.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, scikit-learn is BSD-3-Clause.
- Tags unique to awesome-AutoML: 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 (scikit-learn/scikit-learn) · observed Aug 3, 2026
- GitHub forks (scikit-learn/scikit-learn) · observed Aug 3, 2026
- Last push (scikit-learn/scikit-learn) · observed Aug 1, 2026
- License file (BSD-3-Clause) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 17, 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: scikit-learn 67k · awesome-AutoML 941 (synced Aug 3, 2026).
Common questions
- What is the difference between scikit-learn and awesome-AutoML?
- scikit-learn: machine learning in Python. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose scikit-learn over awesome-AutoML?
- Choose scikit-learn over awesome-AutoML when License: scikit-learn is BSD-3-Clause, awesome-AutoML is GPL-3.0; Tags unique to scikit-learn: data-analysis, data-science, machine-learning, python; When you need a well-documented library with clear examples and strong community support.
- When should I choose awesome-AutoML over scikit-learn?
- Choose awesome-AutoML over scikit-learn when License: awesome-AutoML is GPL-3.0, scikit-learn is BSD-3-Clause; Tags unique to awesome-AutoML: 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 scikit-learn?
- Avoid if you require cutting-edge deep learning capabilities or model training that is more efficiently managed with GPU accelerators. Not ideal when dealing with very large datasets that benefit from out-of-core computation, as it lacks native support for such functionalities. If real-time machine learning predictions are critical and need ultra-low latency, other tools might offer better performance.
- 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 scikit-learn or awesome-AutoML more popular on GitHub?
- scikit-learn has more GitHub stars (66,855 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are scikit-learn and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (scikit-learn: BSD-3-Clause, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to scikit-learn or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at scikit-learn alternatives and awesome-AutoML alternatives (scikit-learn 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, scikit-learn or awesome-AutoML?
- scikit-learn: Very active. 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 scikit-learn and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: scikit-learn trust report; awesome-AutoML trust report.