Comparison
autogluon vs awesome-mlops
Verdict
Pick autogluon if autoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP; pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
Markdown twin · autogluon alternatives · awesome-mlops alternatives
GraphCanon updated Oct 4, 2026
7views this month
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Trust & integrity
| Signal | autogluon | awesome-mlops |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 30, 2026 · github_public_v1 | Steady (48d since push) As of Oct 4, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- autogluon
- Fast and Accurate ML in 3 Lines of Code
- awesome-mlops
- A curated list of awesome MLOps tools.
Stars
- autogluon
- 11k
- awesome-mlops
- 5.3k
Forks
- autogluon
- 1.2k
- awesome-mlops
- 792
Open issues
- autogluon
- 377
- awesome-mlops
- 98
Language
- autogluon
- Python
- awesome-mlops
- Python
Adopt for
- autogluon
- AutoGluon: an automated ML library for Python that promises accuracy in model training with minimal effort, supporting tabular data, time-series forecasting, vision tasks, and NLP.
- awesome-mlops
- Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
Persona
- autogluon
- -
- awesome-mlops
- -
Runtime
- autogluon
- -
- awesome-mlops
- -
License
- autogluon
- Apache-2.0 License allows for both commercial and private use with attribution required but no warranty provided by contributors or authors.
- awesome-mlops
- -
Last pushed
- autogluon
- Sep 28, 2026
- awesome-mlops
- Aug 17, 2026
Categories
- autogluon
- Developer Tools, Model Training
- awesome-mlops
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Maintenance
- autogluon
- Very active (96%)
- awesome-mlops
- Steady (60%)
Days since push
- autogluon
- 1d
- awesome-mlops
- 48d
Open issues (now)
- autogluon
- 377
- awesome-mlops
- 98
Stars delta
- autogluon
- +180 (30d)
- awesome-mlops
- +55 (30d)
Open issues delta
- autogluon
- -11 (30d)
- awesome-mlops
- +27 (30d)
Owner type
- autogluon
- Organization
- awesome-mlops
- User
Full report
- autogluon
- Trust report
- awesome-mlops
- Trust report
Shared compatibility
- Python · autogluon: Python runtime · awesome-mlops: Python runtime
Choose autogluon if…
- Tags unique to autogluon: automated-machine-learning, automl, computer-vision, deep-learning.
- When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis.
- More GitHub stars (11k vs 5.3k) - visibility, not fit.
When NOT to use autogluon
- If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation.
- For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this.
Choose awesome-mlops if…
- Tags unique to awesome-mlops: ai, awesome, machine-learning, machine-learning-engineering.
- Also covers Evaluation & Observability, Inference & Serving.
- You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
When NOT to use awesome-mlops
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform.
- Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (autogluon/autogluon) · observed Sep 30, 2026
- GitHub forks (autogluon/autogluon) · observed Sep 30, 2026
- Last push (autogluon/autogluon) · observed Sep 28, 2026
- License file (Apache-2.0) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (kelvins/awesome-mlops) · observed Oct 4, 2026
- GitHub forks (kelvins/awesome-mlops) · observed Oct 4, 2026
- Last push (kelvins/awesome-mlops) · observed Aug 17, 2026
- License file (unknown) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: autogluon 11k · awesome-mlops 5.3k (synced Sep 30, 2026).
Common questions
- What is the difference between autogluon and awesome-mlops?
- autogluon: Fast and Accurate ML in 3 Lines of Code. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories.
- When should I choose autogluon over awesome-mlops?
- Choose autogluon over awesome-mlops when Tags unique to autogluon: automated-machine-learning, automl, computer-vision, deep-learning; When you need quick setup of complex ML workflows involving CV, NLP, or structured data analysis; More GitHub stars (11k vs 5.3k) - visibility, not fit.
- When should I choose awesome-mlops over autogluon?
- Choose awesome-mlops over autogluon when Tags unique to awesome-mlops: ai, awesome, machine-learning, machine-learning-engineering; Also covers Evaluation & Observability, Inference & Serving; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
- When should I avoid autogluon?
- If your environment does not support Python versions 3.10-3.13 as AutoGluon requires these specific versions for operation. For custom model developments where low-level control over every aspect of the ML process is a priority, given that AutoGluon automates significant parts of this.
- When should I avoid awesome-mlops?
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
- Is autogluon or awesome-mlops more popular on GitHub?
- autogluon has more GitHub stars (10,756 vs 5,284). Stars measure visibility, not whether either tool fits your constraints.
- Are autogluon and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to autogluon or awesome-mlops?
- GraphCanon lists graph-backed alternatives at autogluon alternatives and awesome-mlops alternatives (autogluon markdown twin, awesome-mlops 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, autogluon or awesome-mlops?
- autogluon: Very active. awesome-mlops: Steady. 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 autogluon and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autogluon trust report; awesome-mlops trust report.