--- title: "awesome-mlops vs pipelines" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelvins-awesome-mlops-vs-kubeflow-pipelines" tools: ["kelvins-awesome-mlops", "kubeflow-pipelines"] --- # awesome-mlops vs pipelines *GraphCanon updated Aug 4, 2026* ## 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. [awesome-mlops](https://github.com/kelvins/awesome-mlops) reports 5.2k GitHub stars, 762 forks, and 71 open issues, last pushed Apr 29, 2026. [pipelines](https://www.kubeflow.org/docs/components/pipelines/) has 4.2k stars, 2.1k forks, and 512 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops) and [pipelines's repository](https://github.com/kubeflow/pipelines). | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [pipelines](/tools/kubeflow-pipelines.md) | | --- | --- | --- | | Tagline | A curated list of awesome MLOps tools. | Machine Learning Pipelines for Kubeflow | | Stars | 5,229 | 4,173 | | Forks | 762 | 2,075 | | Open issues | 71 | 512 | | Language | Python | Python | | Adopt for | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | Pipelines from Kubeflow is optimized for Kubernetes environments and supports MLOps workflows with Emissary Executor by default. | | 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 | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [pipelines](/tools/kubeflow-pipelines.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 97d | 0d | | Open issues (now) | 71 | 512 | | Owner type | User | Organization | | Full report | [trust report](/tools/kelvins-awesome-mlops/trust.md) | [trust report](/tools/kubeflow-pipelines/trust.md) | ## Decision facts: awesome-mlops - **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. ## 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： ## Choose when ### Choose awesome-mlops if… - 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. ### 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). ## 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. ## 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. ## 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 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 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 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 (5,229 vs 4,173). 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](/tools/kelvins-awesome-mlops/alternatives) and [pipelines alternatives](/tools/kubeflow-pipelines/alternatives) ([awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/alternatives.md), [pipelines markdown twin](/tools/kubeflow-pipelines/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/kelvins-awesome-mlops-vs-kubeflow-pipelines.md) 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: Slowing. 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](/tools/kelvins-awesome-mlops/trust); [pipelines trust report](/tools/kubeflow-pipelines/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kelvins-awesome-mlops`](/api/graphcanon/graph?tool=kelvins-awesome-mlops) - 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/_