Comparison
autoai vs metric-learn
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.
Markdown twin · autoai alternatives · metric-learn alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | autoai | metric-learn |
|---|---|---|
| Maintenance | Dormant (496d since push) As of 2w · github_public_v1 | Slowing (136d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings 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
- 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
Stars
- autoai
- 186
- metric-learn
- 1.4k
Forks
- autoai
- 46
- metric-learn
- 231
Open issues
- autoai
- 9
- metric-learn
- 51
Language
- autoai
- Python
- metric-learn
- Python
Adopt for
- autoai
- Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation.
- 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
- autoai
- -
- metric-learn
- -
Runtime
- autoai
- -
- metric-learn
- -
License
- autoai
- Apache-2.0
- metric-learn
- MIT
Last pushed
- autoai
- Mar 25, 2025
- metric-learn
- Mar 19, 2026
Categories
- autoai
- Model Training
- metric-learn
- Model Training
Trust and health
Maintenance
- autoai
- Dormant (18%)
- metric-learn
- Slowing (36%)
Days since push
- autoai
- 496d
- metric-learn
- 136d
Open issues (now)
- autoai
- 9
- metric-learn
- 51
OSV dependency advisories
- autoai
- Published findings
- metric-learn
- No lockfile (source not queried)
Full report
- autoai
- Trust report
- metric-learn
- Trust report
Shared compatibility
- Python · autoai: Python runtime · metric-learn: Python runtime
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.
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.
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 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 (blobcity/autoai) · observed Aug 4, 2026
- GitHub forks (blobcity/autoai) · observed Aug 4, 2026
- Last push (blobcity/autoai) · observed Mar 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: autoai 186 · metric-learn 1.4k (synced Aug 4, 2026).
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 and metric-learn alternatives (autoai 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, 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; metric-learn trust report.