Comparison
autokeras vs metric-learn
Verdict
Pick autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+; 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.
Markdown twin · autokeras alternatives · metric-learn alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | autokeras | metric-learn |
|---|---|---|
| Maintenance | Slowing (251d since push) As of 3w · github_public_v1 | Slowing (136d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · 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
- autokeras
- AutoML library for deep learning
- metric-learn
- Metric learning algorithms in Python
Stars
- autokeras
- 9.3k
- metric-learn
- 1.4k
Forks
- autokeras
- 1.4k
- metric-learn
- 231
Open issues
- autokeras
- 161
- metric-learn
- 51
Language
- autokeras
- Python
- metric-learn
- Python
Adopt for
- autokeras
- AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+.
- 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.
Persona
- autokeras
- -
- metric-learn
- -
Runtime
- autokeras
- -
- metric-learn
- -
License
- autokeras
- Apache-2.0
- metric-learn
- MIT
Last pushed
- autokeras
- Nov 25, 2025
- metric-learn
- Mar 19, 2026
Categories
- autokeras
- Developer Tools, Model Training
- metric-learn
- Model Training
Trust and health
Days since push
- autokeras
- 251d
- metric-learn
- 136d
Open issues (now)
- autokeras
- 161
- metric-learn
- 51
Full report
- autokeras
- Trust report
- metric-learn
- Trust report
Shared compatibility
- Python · autokeras: Python runtime · metric-learn: Python runtime
Choose autokeras if…
- License: autokeras is Apache-2.0, metric-learn is MIT.
- Tags unique to autokeras: autodl, automl, deep-learning, keras.
- Also covers Developer Tools.
- When your project involves deep learning tasks requiring minimal manual intervention in designing models.
When NOT to use autokeras
- When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible.
- If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.
Choose metric-learn if…
- License: metric-learn is MIT, autokeras is Apache-2.0.
- 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: 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (keras-team/autokeras) · observed Aug 4, 2026
- GitHub forks (keras-team/autokeras) · observed Aug 4, 2026
- Last push (keras-team/autokeras) · observed Nov 25, 2025
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: autokeras 9.3k · metric-learn 1.4k (synced Aug 4, 2026).
Common questions
- What is the difference between autokeras and metric-learn?
- autokeras: AutoML library for deep learning. metric-learn: Metric learning algorithms in Python. See the comparison table for live GitHub stats and shared categories.
- When should I choose autokeras over metric-learn?
- Choose autokeras over metric-learn when License: autokeras is Apache-2.0, metric-learn is MIT; Tags unique to autokeras: autodl, automl, deep-learning, keras; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models.
- When should I choose metric-learn over autokeras?
- Choose metric-learn over autokeras when License: metric-learn is MIT, autokeras is Apache-2.0; 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: 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 avoid autokeras?
- When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.
- 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.
- Is autokeras or metric-learn more popular on GitHub?
- autokeras has more GitHub stars (9,328 vs 1,438). Stars measure visibility, not whether either tool fits your constraints.
- Are autokeras and metric-learn open source?
- Yes - both are open-source projects on GitHub (autokeras: Apache-2.0, metric-learn: MIT).
- Where can I find alternatives to autokeras or metric-learn?
- GraphCanon lists graph-backed alternatives at autokeras alternatives and metric-learn alternatives (autokeras markdown twin, metric-learn 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, autokeras or metric-learn?
- autokeras: Slowing. metric-learn: 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 autokeras and metric-learn?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autokeras trust report; metric-learn trust report.