Comparison
data-prep-kit vs awesome-AutoML
Verdict
Pick data-prep-kit if curated decision-critical facts for the tool 'data-prep-kit'; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · data-prep-kit alternatives · awesome-AutoML alternatives
GraphCanon updated Oct 4, 2026
10views this month
vs
Trust & integrity
| Signal | data-prep-kit | awesome-AutoML |
|---|---|---|
| Maintenance | Very active (2d since push) As of Sep 6, 2026 · github_public_v1 | Slowing (194d since push) As of Oct 4, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 6, 2026 · github_public_v1 | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- data-prep-kit
- Open source project for data preparation for GenAI applications
- awesome-AutoML
- Curating AutoML research and resources
Stars
- data-prep-kit
- 958
- awesome-AutoML
- 944
Forks
- data-prep-kit
- 253
- awesome-AutoML
- 157
Open issues
- data-prep-kit
- 231
- awesome-AutoML
- 1
Language
- data-prep-kit
- HTML
- awesome-AutoML
- -
Adopt for
- data-prep-kit
- Curated decision-critical facts for the tool 'data-prep-kit'.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- data-prep-kit
- -
- awesome-AutoML
- -
Runtime
- data-prep-kit
- -
- awesome-AutoML
- -
License
- data-prep-kit
- Apache-2.0 license allows users to freely modify and distribute the software, provided that all copyright and permission notices are kept intact.
- awesome-AutoML
- GPL-3.0
Last pushed
- data-prep-kit
- Sep 3, 2026
- awesome-AutoML
- Mar 24, 2026
Categories
- data-prep-kit
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- data-prep-kit
- Very active (96%)
- awesome-AutoML
- Slowing (36%)
Days since push
- data-prep-kit
- 2d
- awesome-AutoML
- 194d
Open issues (now)
- data-prep-kit
- 231
- awesome-AutoML
- 1
Stars delta
- data-prep-kit
- +6 (30d)
- awesome-AutoML
- +3 (30d)
Open issues delta
- data-prep-kit
- +8 (30d)
- awesome-AutoML
- 0 (30d)
Owner type
- data-prep-kit
- Organization
- awesome-AutoML
- User
Full report
- data-prep-kit
- Trust report
- awesome-AutoML
- Trust report
Choose data-prep-kit if…
- License: data-prep-kit is Apache-2.0, awesome-AutoML is GPL-3.0.
- Requirements: Installation requires Python versions from 3.10 to 3.13..
- Tags unique to data-prep-kit: code-quality, data-prep, data-preparation, data-preprocessing-pipelines.
- Use data-prep-kit when you are working with large language models (LLMs) or other GenAI applications and need comprehensive tools for data preparation, including deduplication and fine-tuning.
When NOT to use data-prep-kit
- Avoid data-prep-kit if the project does not require Python compatibility or if Python versions earlier than 3.10 are in use since this toolkit supports only from Python 3.10 to 3.13.
- Do not use it for tasks unrelated to GenAI applications as its specific features may not be beneficial.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, data-prep-kit is Apache-2.0.
- 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 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 (data-prep-kit/data-prep-kit) · observed Sep 20, 2026
- GitHub forks (data-prep-kit/data-prep-kit) · observed Sep 20, 2026
- Last push (data-prep-kit/data-prep-kit) · observed Sep 3, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (windmaple/awesome-AutoML) · observed Oct 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Oct 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: data-prep-kit 958 · awesome-AutoML 944 (synced Sep 20, 2026).
Common questions
- What is the difference between data-prep-kit and awesome-AutoML?
- data-prep-kit: Open source project for data preparation for GenAI applications. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose data-prep-kit over awesome-AutoML?
- Choose data-prep-kit over awesome-AutoML when License: data-prep-kit is Apache-2.0, awesome-AutoML is GPL-3.0; Requirements: Installation requires Python versions from 3.10 to 3.13.; Tags unique to data-prep-kit: code-quality, data-prep, data-preparation, data-preprocessing-pipelines; Use data-prep-kit when you are working with large language models (LLMs) or other GenAI applications and need comprehensive tools for data preparation, including deduplication and fine-tuning.
- When should I choose awesome-AutoML over data-prep-kit?
- Choose awesome-AutoML over data-prep-kit when License: awesome-AutoML is GPL-3.0, data-prep-kit is Apache-2.0; 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 data-prep-kit?
- Avoid data-prep-kit if the project does not require Python compatibility or if Python versions earlier than 3.10 are in use since this toolkit supports only from Python 3.10 to 3.13. Do not use it for tasks unrelated to GenAI applications as its specific features may not be beneficial.
- 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 data-prep-kit or awesome-AutoML more popular on GitHub?
- data-prep-kit has more GitHub stars (958 vs 944). Stars measure visibility, not whether either tool fits your constraints.
- Are data-prep-kit and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (data-prep-kit: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to data-prep-kit or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at data-prep-kit alternatives and awesome-AutoML alternatives (data-prep-kit 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, data-prep-kit or awesome-AutoML?
- data-prep-kit: 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 data-prep-kit and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: data-prep-kit trust report; awesome-AutoML trust report.