GraphCanon updated Oct 4, 2026 · GitHub synced Oct 4, 2026
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Decision brief
Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Good fit when
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- For discovering tools and papers that focus on reducing resource constraints for tabular datasets.
Avoid when
- 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.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (194d since push)
- As of Oct 4, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 4, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/windmaple/awesome-AutoMLSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A collection of Automated Machine Learning (AutoML) related tools, projects, research papers, focusing on topics like neural architecture search, hyperparameter optimization, and meta-learning.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Tags
README
Awesome AutoML Curating a list of AutoML related research, tools, projects and other resources AutoML AutoML is the tools and technology to use machine learning methods and processes to automate machine learning systems and make them more accessible. It existed for several decade...
For agents
This page has a .md twin and JSON over the API.