--- title: "Made-With-ML vs pipelines" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-kubeflow-pipelines" tools: ["gokumohandas-made-with-ml", "kubeflow-pipelines"] --- # Made-With-ML vs pipelines *GraphCanon updated Aug 14, 2026* ## Verdict Pick Made-With-ML if made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows; pick pipelines if pipelines from Kubeflow is optimized for Kubernetes environments and supports MLOps workflows with Emissary Executor by default. [Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 26 open issues, last pushed Mar 4, 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 [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [pipelines's repository](https://github.com/kubeflow/pipelines). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [pipelines](/tools/kubeflow-pipelines.md) | | --- | --- | --- | | Tagline | Learn to develop, deploy and iterate on production-grade ML applications | Machine Learning Pipelines for Kubeflow | | Stars | 49,074 | 4,173 | | Forks | 7,710 | 2,075 | | Open issues | 26 | 512 | | Language | Jupyter Notebook | Python | | Adopt for | Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. | Pipelines from Kubeflow is optimized for Kubernetes environments and supports MLOps workflows with Emissary Executor by default. | | Persona | - | - | | Runtime | - | - | | License | MIT | 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, Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [pipelines](/tools/kubeflow-pipelines.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 162d | 0d | | Open issues (now) | 26 | 512 | | Stars delta | +371 (30d) | Unknown | | Open issues delta | -1 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/kubeflow-pipelines/trust.md) | ## Decision facts: Made-With-ML - **Requirements:** A foundational understanding of Python programming is required to fully benefit from the learning resources provided. - **Adopt for:** Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. ## 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 Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; pipelines is Python. - License: Made-With-ML is MIT, pipelines is Apache-2.0. - Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.. - Tags unique to Made-With-ML: data-engineering, data-quality, deep-learning, distributed-ml. - Also covers Developer Tools. - If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ### Choose pipelines if… - pipelines is primarily Python; Made-With-ML is Jupyter Notebook. - License: pipelines is Apache-2.0, Made-With-ML is MIT. - 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 Made-With-ML - If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch. - For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support. ## 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 Made-With-ML and pipelines? Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. pipelines: Machine Learning Pipelines for Kubeflow. See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over pipelines? Choose Made-With-ML over pipelines when Made-With-ML is primarily Jupyter Notebook; pipelines is Python; License: Made-With-ML is MIT, pipelines is Apache-2.0; Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.; Tags unique to Made-With-ML: data-engineering, data-quality, deep-learning, distributed-ml; Also covers Developer Tools; If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ### When should I choose pipelines over Made-With-ML? Choose pipelines over Made-With-ML when pipelines is primarily Python; Made-With-ML is Jupyter Notebook; License: pipelines is Apache-2.0, Made-With-ML is MIT; 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 Made-With-ML? If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch. For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support. ### 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 Made-With-ML or pipelines more popular on GitHub? Made-With-ML has more GitHub stars (49,074 vs 4,173). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and pipelines open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, pipelines: Apache-2.0). ### Where can I find alternatives to Made-With-ML or pipelines? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [pipelines alternatives](/tools/kubeflow-pipelines/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/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/gokumohandas-made-with-ml-vs-kubeflow-pipelines.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Made-With-ML or pipelines? Made-With-ML: 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 Made-With-ML and pipelines? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [pipelines trust report](/tools/kubeflow-pipelines/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gokumohandas-made-with-ml`](/api/graphcanon/graph?tool=gokumohandas-made-with-ml) - 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/_