---
title: "scikit-learn vs awesome-AutoML"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/scikit-learn-scikit-learn-vs-windmaple-awesome-automl"
tools: ["scikit-learn-scikit-learn", "windmaple-awesome-automl"]
---

# scikit-learn vs awesome-AutoML

*GraphCanon updated Aug 4, 2026*

## Verdict

Pick scikit-learn if use scikit-learn for Python-based machine learning tasks that require robust algorithms, comprehensive documentation, and extensive community support; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.

[scikit-learn](https://scikit-learn.org) reports 67k GitHub stars, 27k forks, and 2.1k open issues, last pushed Aug 1, 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 [scikit-learn's repository](https://github.com/scikit-learn/scikit-learn) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML).

| | [scikit-learn](/tools/scikit-learn-scikit-learn.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) |
| --- | --- | --- |
| Tagline | machine learning in Python | Curating AutoML research and resources |
| Stars | 66,855 | 941 |
| Forks | 27,251 | 156 |
| Open issues | 2,115 | 1 |
| Language | Python | - |
| Adopt for | Use scikit-learn for Python-based machine learning tasks that require robust algorithms, comprehensive documentation, and extensive community support. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. |
| Persona | - | - |
| Runtime | - | - |
| License | BSD-3-Clause | GPL-3.0 |
| Categories | Model Training | Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [scikit-learn](/tools/scikit-learn-scikit-learn.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Slowing (36%) |
| Days since push | 1d | 133d |
| Open issues (now) | 2.1k | 1 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/scikit-learn-scikit-learn/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) |

## Decision facts: scikit-learn

- **Adopt for:** Use scikit-learn for Python-based machine learning tasks that require robust algorithms, comprehensive documentation, and extensive community support.

## Decision facts: awesome-AutoML

- **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.

## Choose when

### Choose scikit-learn if…

- License: scikit-learn is BSD-3-Clause, awesome-AutoML is GPL-3.0.
- Tags unique to scikit-learn: data-analysis, data-science, machine-learning, python.
- When you need a well-documented library with clear examples and strong community support.

### Choose awesome-AutoML if…

- License: awesome-AutoML is GPL-3.0, scikit-learn is BSD-3-Clause.
- 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 scikit-learn

- Avoid if you require cutting-edge deep learning capabilities or model training that is more efficiently managed with GPU accelerators.
- Not ideal when dealing with very large datasets that benefit from out-of-core computation, as it lacks native support for such functionalities.
- If real-time machine learning predictions are critical and need ultra-low latency, other tools might offer better performance.

## 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 scikit-learn and awesome-AutoML?

scikit-learn: machine learning in Python. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.

### When should I choose scikit-learn over awesome-AutoML?

Choose scikit-learn over awesome-AutoML when License: scikit-learn is BSD-3-Clause, awesome-AutoML is GPL-3.0; Tags unique to scikit-learn: data-analysis, data-science, machine-learning, python; When you need a well-documented library with clear examples and strong community support.

### When should I choose awesome-AutoML over scikit-learn?

Choose awesome-AutoML over scikit-learn when License: awesome-AutoML is GPL-3.0, scikit-learn is BSD-3-Clause; 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 scikit-learn?

Avoid if you require cutting-edge deep learning capabilities or model training that is more efficiently managed with GPU accelerators. Not ideal when dealing with very large datasets that benefit from out-of-core computation, as it lacks native support for such functionalities. If real-time machine learning predictions are critical and need ultra-low latency, other tools might offer better performance.

### 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 scikit-learn or awesome-AutoML more popular on GitHub?

scikit-learn has more GitHub stars (66,855 vs 941). Stars measure visibility, not whether either tool fits your constraints.

### Are scikit-learn and awesome-AutoML open source?

Yes - both are open-source projects on GitHub (scikit-learn: BSD-3-Clause, awesome-AutoML: GPL-3.0).

### Where can I find alternatives to scikit-learn or awesome-AutoML?

GraphCanon lists graph-backed alternatives at [scikit-learn alternatives](/tools/scikit-learn-scikit-learn/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([scikit-learn markdown twin](/tools/scikit-learn-scikit-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-scikit-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, scikit-learn or awesome-AutoML?

scikit-learn: Very active. 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 scikit-learn and awesome-AutoML?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [scikit-learn trust report](/tools/scikit-learn-scikit-learn/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=scikit-learn-scikit-learn`](/api/graphcanon/graph?tool=scikit-learn-scikit-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/_