--- title: "data-prep-kit vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/data-prep-kit-data-prep-kit-vs-windmaple-awesome-automl" tools: ["data-prep-kit-data-prep-kit", "windmaple-awesome-automl"] --- # data-prep-kit vs awesome-AutoML *GraphCanon updated Aug 7, 2026* ## 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. [data-prep-kit](https://data-prep-kit.github.io/data-prep-kit/) reports 952 GitHub stars, 253 forks, and 223 open issues, last pushed Jul 14, 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 [data-prep-kit's repository](https://github.com/data-prep-kit/data-prep-kit) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [data-prep-kit](/tools/data-prep-kit-data-prep-kit.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | Open source project for data preparation for GenAI applications | Curating AutoML research and resources | | Stars | 952 | 941 | | Forks | 253 | 156 | | Open issues | 223 | 1 | | Language | HTML | - | | Adopt for | Curated decision-critical facts for the tool 'data-prep-kit'. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 license allows users to freely modify and distribute the software, provided that all copyright and permission notices are kept intact. | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [data-prep-kit](/tools/data-prep-kit-data-prep-kit.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 23d | 133d | | Open issues (now) | 223 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/data-prep-kit-data-prep-kit/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: data-prep-kit - **Requirements:** Installation requires Python versions from 3.10 to 3.13. - **Adopt for:** Curated decision-critical facts for the tool 'data-prep-kit'. - **License detail:** Apache-2.0 license allows users to freely modify and distribute the software, provided that all copyright and permission notices are kept intact. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### 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. ### 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 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 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 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 (952 vs 941). 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](/tools/data-prep-kit-data-prep-kit/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([data-prep-kit markdown twin](/tools/data-prep-kit-data-prep-kit/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/data-prep-kit-data-prep-kit-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, data-prep-kit or awesome-AutoML? data-prep-kit: 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](/tools/data-prep-kit-data-prep-kit/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=data-prep-kit-data-prep-kit`](/api/graphcanon/graph?tool=data-prep-kit-data-prep-kit) - 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/_