Comparison
pipelines vs awesome-mlops
Verdict
Pick pipelines if pipelines from Kubeflow is optimized for Kubernetes environments and supports MLOps workflows with Emissary Executor by default; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Markdown twin · pipelines alternatives · awesome-mlops alternatives
GraphCanon updated Oct 4, 2026
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Trust & integrity
| Signal | pipelines | awesome-mlops |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 29, 2026 · github_public_v1 | Dormant (681d since push) As of Oct 4, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 29, 2026 · github_public_v1 | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings 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
- pipelines
- Machine Learning Pipelines for Kubeflow
- awesome-mlops
- A curated list of references for MLOps
Stars
- pipelines
- 4.2k
- awesome-mlops
- 14k
Forks
- pipelines
- 2.1k
- awesome-mlops
- 2.1k
Open issues
- pipelines
- 359
- awesome-mlops
- 47
Language
- pipelines
- Go
- awesome-mlops
- -
Adopt for
- pipelines
- Pipelines from Kubeflow is optimized for Kubernetes environments and supports MLOps workflows with Emissary Executor by default.
- awesome-mlops
- awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Persona
- pipelines
- -
- awesome-mlops
- -
Runtime
- pipelines
- -
- awesome-mlops
- -
License
- pipelines
- Apache-2.0 license offers permissive terms for distribution and modification, allowing proprietary衍生结束于此。许可证的总结应完整并准确。让我们纠正这一点，并继续其他字段的信息提取和总结：
- awesome-mlops
- -
Last pushed
- pipelines
- Sep 28, 2026
- awesome-mlops
- Nov 21, 2024
Categories
- pipelines
- Inference & Serving, Model Training
- awesome-mlops
- Inference & Serving, Model Training
Trust and health
Maintenance
- pipelines
- Very active (96%)
- awesome-mlops
- Dormant (18%)
Days since push
- pipelines
- 0d
- awesome-mlops
- 681d
Open issues (now)
- pipelines
- 359
- awesome-mlops
- 47
Stars delta
- pipelines
- +57 (30d)
- awesome-mlops
- +101 (30d)
Open issues delta
- pipelines
- -153 (30d)
- awesome-mlops
- +3 (30d)
Owner type
- pipelines
- Organization
- awesome-mlops
- User
OSV dependency advisories
- pipelines
- Published findings
- awesome-mlops
- No lockfile (source not queried)
Full report
- pipelines
- Trust report
- awesome-mlops
- Trust report
Choose pipelines if…
- Tags unique to pipelines: kubernetes, kubflow-pipelines.
- Use Pipelines when you are working in an existing Kubernetes cluster as it integrates seamlessly without any configuration hassles specific to container runtimes like Docker.
- More recently updated (last pushed Sep 28, 2026).
When NOT to use pipelines
- Avoid Pipelines from Kubeflow if you require support on a non-Kubernetes environment, as it is tightly integrated with Kubernetes services.
- Do not use this tool if your operations necessitate legacy Docker container runtime integration without the adaptability provided by Emissary Executor.
Choose awesome-mlops if…
- Tags unique to awesome-mlops: ai, devops, engineering, federated-learning.
- If you need references covering online training and inference service architecture patterns, consider awesome-mlops.
- More GitHub stars (14k vs 4.2k) - visibility, not fit.
When NOT to use awesome-mlops
- Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list.
- Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (kubeflow/pipelines) · observed Sep 29, 2026
- GitHub forks (kubeflow/pipelines) · observed Sep 29, 2026
- Last push (kubeflow/pipelines) · observed Sep 28, 2026
- License file (Apache-2.0) · observed Sep 29, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (visenger/awesome-mlops) · observed Oct 4, 2026
- GitHub forks (visenger/awesome-mlops) · observed Oct 4, 2026
- Last push (visenger/awesome-mlops) · observed Nov 21, 2024
- 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: pipelines 4.2k · awesome-mlops 14k (synced Sep 29, 2026).
Common questions
- What is the difference between pipelines and awesome-mlops?
- pipelines: Machine Learning Pipelines for Kubeflow. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
- When should I choose pipelines over awesome-mlops?
- Choose pipelines over awesome-mlops when Tags unique to pipelines: kubernetes, kubflow-pipelines; Use Pipelines when you are working in an existing Kubernetes cluster as it integrates seamlessly without any configuration hassles specific to container runtimes like Docker; More recently updated (last pushed Sep 28, 2026).
- When should I choose awesome-mlops over pipelines?
- Choose awesome-mlops over pipelines when Tags unique to awesome-mlops: ai, devops, engineering, federated-learning; If you need references covering online training and inference service architecture patterns, consider awesome-mlops; More GitHub stars (14k vs 4.2k) - visibility, not fit.
- When should I avoid pipelines?
- Avoid Pipelines from Kubeflow if you require support on a non-Kubernetes environment, as it is tightly integrated with Kubernetes services. Do not use this tool if your operations necessitate legacy Docker container runtime integration without the adaptability provided by Emissary Executor.
- When should I avoid awesome-mlops?
- Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
- Is pipelines or awesome-mlops more popular on GitHub?
- awesome-mlops has more GitHub stars (14,228 vs 4,230). Stars measure visibility, not whether either tool fits your constraints.
- Are pipelines and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to pipelines or awesome-mlops?
- GraphCanon lists graph-backed alternatives at pipelines alternatives and awesome-mlops alternatives (pipelines 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, pipelines or awesome-mlops?
- pipelines: Very active. awesome-mlops: 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 pipelines and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: pipelines trust report; awesome-mlops trust report.