Comparison
evalml vs awesome-automl-papers
Verdict
Pick evalml if evalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license; pick awesome-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.
Markdown twin · evalml alternatives · awesome-automl-papers alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | evalml | awesome-automl-papers |
|---|---|---|
| Maintenance | Slowing (201d since push) As of 2w · github_public_v1 | Dormant (784d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · 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
- evalml
- An AutoML library written in Python
- awesome-automl-papers
- A curated list of automated machine learning papers and resources.
Stars
- evalml
- 852
- awesome-automl-papers
- 4.2k
Forks
- evalml
- 93
- awesome-automl-papers
- 678
Open issues
- evalml
- 324
- awesome-automl-papers
- 2
Language
- evalml
- Python
- awesome-automl-papers
- -
Adopt for
- evalml
- EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license.
- awesome-automl-papers
- awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.
Persona
- evalml
- -
- awesome-automl-papers
- -
Runtime
- evalml
- -
- awesome-automl-papers
- -
License
- evalml
- EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices.
- awesome-automl-papers
- Apache-2.0
Last pushed
- evalml
- Jan 14, 2026
- awesome-automl-papers
- Jun 11, 2024
Categories
- evalml
- Evaluation & Observability, Model Training
- awesome-automl-papers
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- evalml
- Slowing (36%)
- awesome-automl-papers
- Dormant (18%)
Days since push
- evalml
- 201d
- awesome-automl-papers
- 784d
Open issues (now)
- evalml
- 324
- awesome-automl-papers
- 2
Owner type
- evalml
- Organization
- awesome-automl-papers
- User
Full report
- evalml
- Trust report
- awesome-automl-papers
- Trust report
Choose evalml if…
- License: evalml is BSD-3-Clause, awesome-automl-papers is Apache-2.0.
- Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased..
- Requirements: Min 2 GB RAM.
- Tags unique to evalml: data-science, feature-selection, hyperparameter-tuning, machine-learning.
- You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical.
When NOT to use evalml
- You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box.
- Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities.
Choose awesome-automl-papers if…
- License: awesome-automl-papers is Apache-2.0, evalml is BSD-3-Clause.
- Tags unique to awesome-automl-papers: hyperparameter-optimization, neural-architecture-search.
- When you need a curated list of academic materials to research or learn about AutoML technologies
When NOT to use awesome-automl-papers
- If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources
- When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (alteryx/evalml) · observed Aug 4, 2026
- GitHub forks (alteryx/evalml) · observed Aug 4, 2026
- Last push (alteryx/evalml) · observed Jan 14, 2026
- License file (BSD-3-Clause) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (hibayesian/awesome-automl-papers) · observed Aug 4, 2026
- GitHub forks (hibayesian/awesome-automl-papers) · observed Aug 4, 2026
- Last push (hibayesian/awesome-automl-papers) · observed Jun 11, 2024
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: evalml 852 · awesome-automl-papers 4.2k (synced Aug 4, 2026).
Common questions
- What is the difference between evalml and awesome-automl-papers?
- evalml: An AutoML library written in Python. awesome-automl-papers: A curated list of automated machine learning papers and resources.. See the comparison table for live GitHub stats and shared categories.
- When should I choose evalml over awesome-automl-papers?
- Choose evalml over awesome-automl-papers when License: evalml is BSD-3-Clause, awesome-automl-papers is Apache-2.0; Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.; Requirements: Min 2 GB RAM; Tags unique to evalml: data-science, feature-selection, hyperparameter-tuning, machine-learning; You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical.
- When should I choose awesome-automl-papers over evalml?
- Choose awesome-automl-papers over evalml when License: awesome-automl-papers is Apache-2.0, evalml is BSD-3-Clause; Tags unique to awesome-automl-papers: hyperparameter-optimization, neural-architecture-search; When you need a curated list of academic materials to research or learn about AutoML technologies.
- When should I avoid evalml?
- You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box. Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities.
- When should I avoid awesome-automl-papers?
- If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers
- Is evalml or awesome-automl-papers more popular on GitHub?
- awesome-automl-papers has more GitHub stars (4,155 vs 852). Stars measure visibility, not whether either tool fits your constraints.
- Are evalml and awesome-automl-papers open source?
- Yes - both are open-source projects on GitHub (evalml: BSD-3-Clause, awesome-automl-papers: Apache-2.0).
- Where can I find alternatives to evalml or awesome-automl-papers?
- GraphCanon lists graph-backed alternatives at evalml alternatives and awesome-automl-papers alternatives (evalml markdown twin, awesome-automl-papers 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, evalml or awesome-automl-papers?
- evalml: Slowing. awesome-automl-papers: Dormant. 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 evalml and awesome-automl-papers?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: evalml trust report; awesome-automl-papers trust report.