--- title: "metric-learn vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/scikit-learn-contrib-metric-learn-vs-windmaple-awesome-automl" tools: ["scikit-learn-contrib-metric-learn", "windmaple-awesome-automl"] --- # metric-learn vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## 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. [metric-learn](http://contrib.scikit-learn.org/metric-learn/) reports 1.4k GitHub stars, 231 forks, and 51 open issues, last pushed Mar 19, 2026. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 941 stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [metric-learn's repository](https://github.com/scikit-learn-contrib/metric-learn) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [metric-learn](/tools/scikit-learn-contrib-metric-learn.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | Metric learning algorithms in Python | Curating AutoML research and resources | | Stars | 1,438 | 941 | | Forks | 231 | 156 | | Open issues | 51 | 1 | | Language | Python | - | | Adopt for | 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. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | MIT | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [metric-learn](/tools/scikit-learn-contrib-metric-learn.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Days since push | 136d | 133d | | Open issues (now) | 51 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/scikit-learn-contrib-metric-learn/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: metric-learn - **Requirements:** The application requires Python version 3.6 or higher and specific dependencies such as numpy, scipy, and scikit-learn. - **Adopt for:** 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. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/scikit-learn-contrib-metric-learn/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([metric-learn markdown twin](/tools/scikit-learn-contrib-metric-learn/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/alternatives.md)), 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](/compare/scikit-learn-contrib-metric-learn-vs-windmaple-awesome-automl.md) 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](/tools/scikit-learn-contrib-metric-learn/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=scikit-learn-contrib-metric-learn`](/api/graphcanon/graph?tool=scikit-learn-contrib-metric-learn) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_