--- title: "pipelines vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kubeflow-pipelines-vs-visenger-awesome-mlops" tools: ["kubeflow-pipelines", "visenger-awesome-mlops"] --- # pipelines vs awesome-mlops *GraphCanon updated Aug 4, 2026* ## 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. [pipelines](https://www.kubeflow.org/docs/components/pipelines/) reports 4.2k GitHub stars, 2.1k forks, and 512 open issues, last pushed Aug 3, 2026. [awesome-mlops](https://ml-ops.org) has 14k stars, 2.1k forks, and 44 open issues, last pushed Nov 21, 2024. Figures are from public GitHub metadata via [pipelines's repository](https://github.com/kubeflow/pipelines) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [pipelines](/tools/kubeflow-pipelines.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | Machine Learning Pipelines for Kubeflow | A curated list of references for MLOps | | Stars | 4,173 | 14,127 | | Forks | 2,075 | 2,101 | | Open issues | 512 | 44 | | Language | Python | - | | Adopt for | Pipelines from Kubeflow is optimized for Kubernetes environments and supports MLOps workflows with Emissary Executor by default. | awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | 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： | - | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [pipelines](/tools/kubeflow-pipelines.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 621d | | Open issues (now) | 512 | 44 | | Owner type | Organization | User | | Full report | [trust report](/tools/kubeflow-pipelines/trust.md) | [trust report](/tools/visenger-awesome-mlops/trust.md) | ## Decision facts: pipelines - **Adopt for:** Pipelines from Kubeflow is optimized for Kubernetes environments and supports MLOps workflows with Emissary Executor by default. - **License detail:** 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： ## Decision facts: awesome-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## Choose when ### 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 Aug 3, 2026). ### 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 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 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. ## 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 Aug 3, 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 (14,127 vs 4,173). 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](/tools/kubeflow-pipelines/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([pipelines markdown twin](/tools/kubeflow-pipelines/alternatives.md), [awesome-mlops markdown twin](/tools/visenger-awesome-mlops/alternatives.md)), 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](/compare/kubeflow-pipelines-vs-visenger-awesome-mlops.md) 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](/tools/kubeflow-pipelines/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kubeflow-pipelines`](/api/graphcanon/graph?tool=kubeflow-pipelines) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_