Comparison
metric-learn vs awesome-AutoML
Verdict
Pick metric-learn if metric-learn is a Python library for metric learning that offers a range of algorithms compatible with scikit-learn's API and supports various methods like LMNN, ITML, LFDA among others; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · metric-learn alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | metric-learn | awesome-AutoML |
|---|---|---|
| Maintenance | Slowing (136d 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
- metric-learn
- Metric learning algorithms in Python
- awesome-AutoML
- Curating AutoML research and resources
Stars
- metric-learn
- 1.4k
- awesome-AutoML
- 941
Forks
- metric-learn
- 231
- awesome-AutoML
- 156
Open issues
- metric-learn
- 51
- awesome-AutoML
- 1
Language
- metric-learn
- Python
- awesome-AutoML
- -
Adopt for
- metric-learn
- Metric-learn is a Python library for metric learning that offers a range of algorithms compatible with scikit-learn's API and supports various methods like LMNN, ITML, LFDA among others.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- metric-learn
- -
- awesome-AutoML
- -
Runtime
- metric-learn
- -
- awesome-AutoML
- -
License
- metric-learn
- MIT
- awesome-AutoML
- GPL-3.0
Last pushed
- metric-learn
- Mar 19, 2026
- awesome-AutoML
- Mar 24, 2026
Categories
- metric-learn
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Days since push
- metric-learn
- 136d
- awesome-AutoML
- 133d
Open issues (now)
- metric-learn
- 51
- awesome-AutoML
- 1
Owner type
- metric-learn
- Organization
- awesome-AutoML
- User
Full report
- metric-learn
- Trust report
- awesome-AutoML
- Trust report
Choose metric-learn if…
- License: metric-learn is MIT, awesome-AutoML is GPL-3.0.
- Requirements: The application requires Python version 3.6 or higher and specific dependencies such as numpy, scipy, and scikit-learn..
- Tags unique to metric-learn: machine-learning, metric-learning, python, scikit-learn.
- When you need to use specific metric learning techniques such as Large Margin Nearest Neighbor (LMNN) or Neighborhood Components Analysis (NCA), which are implemented efficiently in Python.
When NOT to use metric-learn
- If your development environment does not already use Python, as metric-learn is specific to this language and its ecosystem.
- For applications that require real-time performance critical operations, since the library may rely on computationally intensive algorithms that could affect latency in real-time systems.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, metric-learn is MIT.
- 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-contrib/metric-learn) · observed Aug 3, 2026
- GitHub forks (scikit-learn-contrib/metric-learn) · observed Aug 3, 2026
- Last push (scikit-learn-contrib/metric-learn) · observed Mar 19, 2026
- License file (MIT) · 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: metric-learn 1.4k · awesome-AutoML 941 (synced Aug 3, 2026).
Common questions
- What is the difference between metric-learn and awesome-AutoML?
- metric-learn: Metric learning algorithms in Python. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose metric-learn over awesome-AutoML?
- Choose metric-learn over awesome-AutoML when License: metric-learn is MIT, awesome-AutoML is GPL-3.0; Requirements: The application requires Python version 3.6 or higher and specific dependencies such as numpy, scipy, and scikit-learn.; Tags unique to metric-learn: machine-learning, metric-learning, python, scikit-learn; When you need to use specific metric learning techniques such as Large Margin Nearest Neighbor (LMNN) or Neighborhood Components Analysis (NCA), which are implemented efficiently in Python.
- When should I choose awesome-AutoML over metric-learn?
- Choose awesome-AutoML over metric-learn when License: awesome-AutoML is GPL-3.0, metric-learn is MIT; 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 metric-learn?
- If your development environment does not already use Python, as metric-learn is specific to this language and its ecosystem. For applications that require real-time performance critical operations, since the library may rely on computationally intensive algorithms that could affect latency in real-time systems.
- 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 metric-learn or awesome-AutoML more popular on GitHub?
- metric-learn has more GitHub stars (1,438 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are metric-learn and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (metric-learn: MIT, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to metric-learn or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at metric-learn alternatives and awesome-AutoML alternatives (metric-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, metric-learn or awesome-AutoML?
- metric-learn: Slowing. 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 metric-learn and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: metric-learn trust report; awesome-AutoML trust report.