Comparison
awesome-production-machine-learning vs automl-gs
Verdict
Pick awesome-production-machine-learning when tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment; pick automl-gs when tags unique to automl-gs: automl, keras, machine-learning, python.
Markdown twin · awesome-production-machine-learning alternatives · automl-gs alternatives
GraphCanon updated 2w
awesome-production-machine-learning
EthicalML/awesome-production-machine-learning
★ 21kpushed Aug 1, 2026
Trust & integrity
| Signal | awesome-production-machine-learning | automl-gs |
|---|---|---|
| Maintenance | Very active (3d since push) As of 2w · github_public_v1 | Dormant (2477d 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 | Published findings 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
- awesome-production-machine-learning
- A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning
- automl-gs
- Automatically generate machine-learning models and code with input CSV and target field
Stars
- awesome-production-machine-learning
- 21k
- automl-gs
- 1.9k
Forks
- awesome-production-machine-learning
- 2.6k
- automl-gs
- 181
Open issues
- awesome-production-machine-learning
- 31
- automl-gs
- 28
Language
- awesome-production-machine-learning
- -
- automl-gs
- Python
Adopt for
- awesome-production-machine-learning
- -
- automl-gs
- automl-gs: Python tool for automated machine-learning model creation from CSV data
Persona
- awesome-production-machine-learning
- -
- automl-gs
- -
Runtime
- awesome-production-machine-learning
- -
- automl-gs
- -
License
- awesome-production-machine-learning
- MIT license making it free for use in both personal and commercial projects without requiring royalty payment or source-code disclosure.
- automl-gs
- MIT
Last pushed
- awesome-production-machine-learning
- Aug 1, 2026
- automl-gs
- Oct 22, 2019
Categories
- awesome-production-machine-learning
- Data & Retrieval, Evaluation & Observability, Inference & Serving
- automl-gs
- Data & Retrieval, Model Training
Trust and health
Maintenance
- awesome-production-machine-learning
- Very active (96%)
- automl-gs
- Dormant (18%)
Days since push
- awesome-production-machine-learning
- 3d
- automl-gs
- 2477d
Open issues (now)
- awesome-production-machine-learning
- 31
- automl-gs
- 28
Owner type
- awesome-production-machine-learning
- Organization
- automl-gs
- User
OSV dependency advisories
- awesome-production-machine-learning
- No lockfile (source not queried)
- automl-gs
- Published findings
Full report
- awesome-production-machine-learning
- Trust report
- automl-gs
- Trust report
Choose awesome-production-machine-learning if…
- Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment.
- Also covers Evaluation & Observability, Inference & Serving.
- If you need a diverse set of open-source tools for end-to-end production machine learning tasks
When NOT to use awesome-production-machine-learning
- If you seek a comprehensive solution integrated into one platform rather than selecting from diverse tools
- When your project is specific to only one aspect of machine learning like just deployment or monitoring, and not for the entire workflow
- For teams preferring vendor-specific solutions over open-source options
Choose automl-gs if…
- Tags unique to automl-gs: automl, keras, machine-learning, python.
- Also covers Model Training.
- Need to rapidly prototype models with limited ML expertise
When NOT to use automl-gs
- Complex feature engineering or non-standard data inputs required
- Sensitive about licensing of the generated code
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (EthicalML/awesome-production-machine-learning) · observed Aug 4, 2026
- GitHub forks (EthicalML/awesome-production-machine-learning) · observed Aug 4, 2026
- Last push (EthicalML/awesome-production-machine-learning) · observed Aug 1, 2026
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (minimaxir/automl-gs) · observed Aug 4, 2026
- GitHub forks (minimaxir/automl-gs) · observed Aug 4, 2026
- Last push (minimaxir/automl-gs) · observed Oct 22, 2019
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-production-machine-learning 21k · automl-gs 1.9k (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-production-machine-learning and automl-gs?
- awesome-production-machine-learning: A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning. automl-gs: Automatically generate machine-learning models and code with input CSV and target field. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-production-machine-learning over automl-gs?
- Choose awesome-production-machine-learning over automl-gs when Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment; Also covers Evaluation & Observability, Inference & Serving; If you need a diverse set of open-source tools for end-to-end production machine learning tasks.
- When should I choose automl-gs over awesome-production-machine-learning?
- Choose automl-gs over awesome-production-machine-learning when Tags unique to automl-gs: automl, keras, machine-learning, python; Also covers Model Training; Need to rapidly prototype models with limited ML expertise.
- When should I avoid awesome-production-machine-learning?
- If you seek a comprehensive solution integrated into one platform rather than selecting from diverse tools When your project is specific to only one aspect of machine learning like just deployment or monitoring, and not for the entire workflow For teams preferring vendor-specific solutions over open-source options
- When should I avoid automl-gs?
- Complex feature engineering or non-standard data inputs required Sensitive about licensing of the generated code
- Is awesome-production-machine-learning or automl-gs more popular on GitHub?
- awesome-production-machine-learning has more GitHub stars (20,821 vs 1,869). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-production-machine-learning and automl-gs open source?
- Yes - both are open-source projects on GitHub (awesome-production-machine-learning: MIT, automl-gs: MIT).
- Where can I find alternatives to awesome-production-machine-learning or automl-gs?
- GraphCanon lists graph-backed alternatives at awesome-production-machine-learning alternatives and automl-gs alternatives (awesome-production-machine-learning markdown twin, automl-gs 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, awesome-production-machine-learning or automl-gs?
- awesome-production-machine-learning: Very active. automl-gs: 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 awesome-production-machine-learning and automl-gs?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-production-machine-learning trust report; automl-gs trust report.