Comparison
pachyderm vs awesome-AutoML
Verdict
Pick pachyderm if pachyderm offers a robust platform for managing data-centric pipelines and data versioning with advanced features suitable for analytics and big-data processing in distributed systems; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · pachyderm alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | pachyderm | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (545d 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
- pachyderm
- Data-Centric Pipelines and Data Versioning
- awesome-AutoML
- Curating AutoML research and resources
Stars
- pachyderm
- 6.3k
- awesome-AutoML
- 941
Forks
- pachyderm
- 577
- awesome-AutoML
- 156
Open issues
- pachyderm
- 939
- awesome-AutoML
- 1
Language
- pachyderm
- Go
- awesome-AutoML
- -
Adopt for
- pachyderm
- Pachyderm offers a robust platform for managing data-centric pipelines and data versioning with advanced features suitable for analytics and big-data processing in distributed systems.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- pachyderm
- -
- awesome-AutoML
- -
Runtime
- pachyderm
- -
- awesome-AutoML
- -
License
- pachyderm
- Apache-2.0
- awesome-AutoML
- GPL-3.0
Last pushed
- pachyderm
- Feb 3, 2025
- awesome-AutoML
- Mar 24, 2026
Categories
- pachyderm
- Developer Tools, Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- pachyderm
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- pachyderm
- 545d
- awesome-AutoML
- 133d
Open issues (now)
- pachyderm
- 939
- awesome-AutoML
- 1
Owner type
- pachyderm
- Organization
- awesome-AutoML
- User
Full report
- pachyderm
- Trust report
- awesome-AutoML
- Trust report
Choose pachyderm if…
- License: pachyderm is Apache-2.0, awesome-AutoML is GPL-3.0.
- Pricing: The repository does not specify detailed pricing, but as an open-source tool under the Apache-2.0 license, it is freely available for use and modification..
- Requirements: Min -1 GB RAM; Pachyderm deployment requires a Kubernetes cluster when deployed in production-scale environments..
- Tags unique to pachyderm: analytics, big-data, containers, data-analysis.
- Also covers Developer Tools.
- If you need granular data lineage tracking within your projects, as Pachyderm ensures every transformation is captured.
When NOT to use pachyderm
- If your organization does not require data versioning or cannot benefit from reproducibility features, such as for simple projects with minimal data mutation.
- For scenarios where Docker container management overhead is undesirable; Pachyderm relies heavily on containers and Kubernetes, which might complicate smaller-scale workflows.
- When immediate integration with non-Kubernetes environments is a must. Pachyderm's tight coupling with Kubernetes introduces additional complexity not present in more standalone tools.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, pachyderm 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 (pachyderm/pachyderm) · observed Aug 3, 2026
- GitHub forks (pachyderm/pachyderm) · observed Aug 3, 2026
- Last push (pachyderm/pachyderm) · observed Feb 3, 2025
- License file (Apache-2.0) · 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: pachyderm 6.3k · awesome-AutoML 941 (synced Aug 3, 2026).
Common questions
- What is the difference between pachyderm and awesome-AutoML?
- pachyderm: Data-Centric Pipelines and Data Versioning. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose pachyderm over awesome-AutoML?
- Choose pachyderm over awesome-AutoML when License: pachyderm is Apache-2.0, awesome-AutoML is GPL-3.0; Pricing: The repository does not specify detailed pricing, but as an open-source tool under the Apache-2.0 license, it is freely available for use and modification.; Requirements: Min -1 GB RAM; Pachyderm deployment requires a Kubernetes cluster when deployed in production-scale environments.; Tags unique to pachyderm: analytics, big-data, containers, data-analysis; Also covers Developer Tools; If you need granular data lineage tracking within your projects, as Pachyderm ensures every transformation is captured.
- When should I choose awesome-AutoML over pachyderm?
- Choose awesome-AutoML over pachyderm when License: awesome-AutoML is GPL-3.0, pachyderm 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 pachyderm?
- If your organization does not require data versioning or cannot benefit from reproducibility features, such as for simple projects with minimal data mutation. For scenarios where Docker container management overhead is undesirable; Pachyderm relies heavily on containers and Kubernetes, which might complicate smaller-scale workflows. When immediate integration with non-Kubernetes environments is a must. Pachyderm's tight coupling with Kubernetes introduces additional complexity not present in more standalone tools.
- 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 pachyderm or awesome-AutoML more popular on GitHub?
- pachyderm has more GitHub stars (6,300 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are pachyderm and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (pachyderm: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to pachyderm or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at pachyderm alternatives and awesome-AutoML alternatives (pachyderm 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, pachyderm or awesome-AutoML?
- pachyderm: Dormant. 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 pachyderm and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: pachyderm trust report; awesome-AutoML trust report.