Comparison
featuretools vs awesome-AutoML
Verdict
Pick featuretools if a Python library dedicated to automating feature engineering processes designed to craft features from complex datasets that are interpretable and potentially boost the accuracy of machine learning models; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · featuretools alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | featuretools | awesome-AutoML |
|---|---|---|
| Maintenance | Very active (6d since push) As of 3w · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 2w · 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
- featuretools
- An open source python library for automated feature engineering
- awesome-AutoML
- Curating AutoML research and resources
Stars
- featuretools
- 7.7k
- awesome-AutoML
- 941
Forks
- featuretools
- 915
- awesome-AutoML
- 156
Open issues
- featuretools
- 168
- awesome-AutoML
- 1
Language
- featuretools
- Python
- awesome-AutoML
- -
Adopt for
- featuretools
- A Python library dedicated to automating feature engineering processes designed to craft features from complex datasets that are interpretable and potentially boost the accuracy of machine learning models.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- featuretools
- -
- awesome-AutoML
- -
Runtime
- featuretools
- -
- awesome-AutoML
- -
License
- featuretools
- BSD-3-Clause
- awesome-AutoML
- GPL-3.0
Last pushed
- featuretools
- Jul 27, 2026
- awesome-AutoML
- Mar 24, 2026
Categories
- featuretools
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- featuretools
- Very active (96%)
- awesome-AutoML
- Slowing (36%)
Days since push
- featuretools
- 6d
- awesome-AutoML
- 133d
Open issues (now)
- featuretools
- 168
- awesome-AutoML
- 1
Owner type
- featuretools
- Organization
- awesome-AutoML
- User
Full report
- featuretools
- Trust report
- awesome-AutoML
- Trust report
Choose featuretools if…
- License: featuretools is BSD-3-Clause, awesome-AutoML is GPL-3.0.
- Tags unique to featuretools: automated-feature-engineering, feature-engineering.
- When the goal is to create high-quality features in a semi-automated manner using prior knowledge of relationships within data, enhancing interpretability of machine learning models
When NOT to use featuretools
- If a project requires extremely lightweight solutions that avoid dependencies and overheads associated with complex library packages
- In settings where the underlying data lacks clear relational structure, as Featuretools excels when data relationships are well-defined and can be exploited
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, featuretools is BSD-3-Clause.
- Tags unique to awesome-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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (alteryx/featuretools) · observed Aug 3, 2026
- GitHub forks (alteryx/featuretools) · observed Aug 3, 2026
- Last push (alteryx/featuretools) · observed Jul 27, 2026
- License file (BSD-3-Clause) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: featuretools 7.7k · awesome-AutoML 941 (synced Aug 3, 2026).
Common questions
- What is the difference between featuretools and awesome-AutoML?
- featuretools: An open source python library for automated feature engineering. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose featuretools over awesome-AutoML?
- Choose featuretools over awesome-AutoML when License: featuretools is BSD-3-Clause, awesome-AutoML is GPL-3.0; Tags unique to featuretools: automated-feature-engineering, feature-engineering; When the goal is to create high-quality features in a semi-automated manner using prior knowledge of relationships within data, enhancing interpretability of machine learning models.
- When should I choose awesome-AutoML over featuretools?
- Choose awesome-AutoML over featuretools when License: awesome-AutoML is GPL-3.0, featuretools is BSD-3-Clause; Tags unique to awesome-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 featuretools?
- If a project requires extremely lightweight solutions that avoid dependencies and overheads associated with complex library packages In settings where the underlying data lacks clear relational structure, as Featuretools excels when data relationships are well-defined and can be exploited
- 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 featuretools or awesome-AutoML more popular on GitHub?
- featuretools has more GitHub stars (7,665 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are featuretools and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (featuretools: BSD-3-Clause, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to featuretools or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at featuretools alternatives and awesome-AutoML alternatives (featuretools markdown twin, awesome-AutoML 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, featuretools or awesome-AutoML?
- featuretools: 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 featuretools and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: featuretools trust report; awesome-AutoML trust report.