Comparison
awesome-mlops vs pipelines
Verdict
Pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML; pick pipelines if pipelines from Kubeflow is optimized for Kubernetes environments and supports MLOps workflows with Emissary Executor by default.
Markdown twin · awesome-mlops alternatives · pipelines alternatives
GraphCanon updated Oct 4, 2026
14views this month
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Trust & integrity
| Signal | awesome-mlops | pipelines |
|---|---|---|
| Maintenance | Steady (48d since push) As of Oct 4, 2026 · github_public_v1 | Very active (0d since push) As of Sep 29, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 29, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings 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
- awesome-mlops
- A curated list of awesome MLOps tools.
- pipelines
- Machine Learning Pipelines for Kubeflow
Stars
- awesome-mlops
- 5.3k
- pipelines
- 4.2k
Forks
- awesome-mlops
- 792
- pipelines
- 2.1k
Open issues
- awesome-mlops
- 98
- pipelines
- 359
Language
- awesome-mlops
- Python
- pipelines
- Go
Adopt for
- awesome-mlops
- Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
- pipelines
- Pipelines from Kubeflow is optimized for Kubernetes environments and supports MLOps workflows with Emissary Executor by default.
Persona
- awesome-mlops
- -
- pipelines
- -
Runtime
- awesome-mlops
- -
- pipelines
- -
License
- awesome-mlops
- -
- 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：
Last pushed
- awesome-mlops
- Aug 17, 2026
- pipelines
- Sep 28, 2026
Categories
- awesome-mlops
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
- pipelines
- Inference & Serving, Model Training
Trust and health
Maintenance
- awesome-mlops
- Steady (60%)
- pipelines
- Very active (96%)
Days since push
- awesome-mlops
- 48d
- pipelines
- 0d
Open issues (now)
- awesome-mlops
- 98
- pipelines
- 359
Stars delta
- awesome-mlops
- +55 (30d)
- pipelines
- +57 (30d)
Open issues delta
- awesome-mlops
- +27 (30d)
- pipelines
- -153 (30d)
Owner type
- awesome-mlops
- User
- pipelines
- Organization
OSV dependency advisories
- awesome-mlops
- No lockfile (source not queried)
- pipelines
- Published findings
Full report
- awesome-mlops
- Trust report
- pipelines
- Trust report
Choose awesome-mlops if…
- awesome-mlops is primarily Python; pipelines is Go.
- Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml.
- Also covers Developer Tools, Evaluation & Observability.
- 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.
Choose pipelines if…
- pipelines is primarily Go; awesome-mlops is Python.
- 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.
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (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 on cards: awesome-mlops 5.3k · pipelines 4.2k (synced Oct 4, 2026).
Common questions
- What is the difference between awesome-mlops and pipelines?
- awesome-mlops: A curated list of awesome MLOps tools.. pipelines: Machine Learning Pipelines for Kubeflow. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-mlops over pipelines?
- Choose awesome-mlops over pipelines when awesome-mlops is primarily Python; pipelines is Go; Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml; Also covers Developer Tools, Evaluation & Observability; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
- When should I choose pipelines over awesome-mlops?
- Choose pipelines over awesome-mlops when pipelines is primarily Go; awesome-mlops is Python; 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.
- 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.
- 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.
- Is awesome-mlops or pipelines more popular on GitHub?
- awesome-mlops has more GitHub stars (5,284 vs 4,230). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-mlops and pipelines open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-mlops or pipelines?
- GraphCanon lists graph-backed alternatives at awesome-mlops alternatives and pipelines alternatives (awesome-mlops markdown twin, pipelines 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-mlops or pipelines?
- awesome-mlops: Steady. pipelines: Very active. 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-mlops and pipelines?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-mlops trust report; pipelines trust report.