--- title: "autoai vs metric-learn" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/blobcity-autoai-vs-scikit-learn-contrib-metric-learn" tools: ["blobcity-autoai", "scikit-learn-contrib-metric-learn"] --- # autoai vs metric-learn *GraphCanon updated Aug 4, 2026* ## Verdict Pick autoai if python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation; 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. [autoai](https://github.com/blobcity/autoai) reports 186 GitHub stars, 46 forks, and 9 open issues, last pushed Mar 25, 2025. [metric-learn](http://contrib.scikit-learn.org/metric-learn/) has 1.4k stars, 231 forks, and 51 open issues, last pushed Mar 19, 2026. Figures are from public GitHub metadata via [autoai's repository](https://github.com/blobcity/autoai) and [metric-learn's repository](https://github.com/scikit-learn-contrib/metric-learn). | | [autoai](/tools/blobcity-autoai.md) | [metric-learn](/tools/scikit-learn-contrib-metric-learn.md) | | --- | --- | --- | | Tagline | Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation | Metric learning algorithms in Python | | Stars | 186 | 1,438 | | Forks | 46 | 231 | | Open issues | 9 | 51 | | Language | Python | Python | | Adopt for | Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation. | 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 | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autoai](/tools/blobcity-autoai.md) | [metric-learn](/tools/scikit-learn-contrib-metric-learn.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 496d | 136d | | Open issues (now) | 9 | 51 | | Full report | [trust report](/tools/blobcity-autoai/trust.md) | [trust report](/tools/scikit-learn-contrib-metric-learn/trust.md) | ## Shared compatibility - **Python**: [autoai](/tools/blobcity-autoai.md) - Python runtime; [metric-learn](/tools/scikit-learn-contrib-metric-learn.md) - Python runtime ## Decision facts: autoai - **Adopt for:** Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation. ## 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. ## Choose when ### Choose autoai if… - License: autoai is Apache-2.0, metric-learn is MIT. - Tags unique to autoai: ai, autoai, automl, codegen. - Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets. ### Choose metric-learn if… - License: metric-learn is MIT, autoai 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, 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 autoai - Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing. - Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format. ## 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. ## Common questions ### What is the difference between autoai and metric-learn? autoai: Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation. metric-learn: Metric learning algorithms in Python. See the comparison table for live GitHub stats and shared categories. ### When should I choose autoai over metric-learn? Choose autoai over metric-learn when License: autoai is Apache-2.0, metric-learn is MIT; Tags unique to autoai: ai, autoai, automl, codegen; Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets. ### When should I choose metric-learn over autoai? Choose metric-learn over autoai when License: metric-learn is MIT, autoai 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, 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 autoai? Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing. Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format. ### 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 autoai or metric-learn more popular on GitHub? metric-learn has more GitHub stars (1,438 vs 186). Stars measure visibility, not whether either tool fits your constraints. ### Are autoai and metric-learn open source? Yes - both are open-source projects on GitHub (autoai: Apache-2.0, metric-learn: MIT). ### Where can I find alternatives to autoai or metric-learn? GraphCanon lists graph-backed alternatives at [autoai alternatives](/tools/blobcity-autoai/alternatives) and [metric-learn alternatives](/tools/scikit-learn-contrib-metric-learn/alternatives) ([autoai markdown twin](/tools/blobcity-autoai/alternatives.md), [metric-learn markdown twin](/tools/scikit-learn-contrib-metric-learn/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/blobcity-autoai-vs-scikit-learn-contrib-metric-learn.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, autoai or metric-learn? autoai: Dormant. 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 autoai and metric-learn? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autoai trust report](/tools/blobcity-autoai/trust); [metric-learn trust report](/tools/scikit-learn-contrib-metric-learn/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=blobcity-autoai`](/api/graphcanon/graph?tool=blobcity-autoai) - 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/_