--- title: "Made-With-ML vs mlflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-mlflow-mlflow" tools: ["gokumohandas-made-with-ml", "mlflow-mlflow"] --- # Made-With-ML vs mlflow *GraphCanon updated Sep 20, 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 mlflow if mLflow is an open-source platform for managing the lifecycle of AI and ML models, offering vendor-neutral capabilities for debugging, evaluating, monitoring, and optimizing models across different environments. [Made-With-ML](https://madewithml.com) reports 50k GitHub stars, 7.8k forks, and 25 open issues, last pushed Mar 4, 2026. [mlflow](https://mlflow.org) has 28k stars, 6.3k forks, and 2.1k open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [mlflow's repository](https://github.com/mlflow/mlflow). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [mlflow](/tools/mlflow-mlflow.md) | | --- | --- | --- | | Tagline | Learn to develop, deploy and iterate on production-grade ML applications | Open source AI engineering platform for agents, LLMs, and ML models | | Stars | 49,547 | 28,010 | | Forks | 7,778 | 6,323 | | Open issues | 25 | 2,107 | | 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. | MLflow is an open-source platform for managing the lifecycle of AI and ML models, offering vendor-neutral capabilities for debugging, evaluating, monitoring, and optimizing models across different environments. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Developer Tools, Inference & Serving, Model Training | Developer Tools, Evaluation & Observability, 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) | [mlflow](/tools/mlflow-mlflow.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 199d | 0d | | Open issues (now) | 25 | 2.1k | | Stars delta | +473 (30d) | +895 (30d) | | Open issues delta | -1 (30d) | +31 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/mlflow-mlflow/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: mlflow - **Adopt for:** MLflow is an open-source platform for managing the lifecycle of AI and ML models, offering vendor-neutral capabilities for debugging, evaluating, monitoring, and optimizing models across different environments. ## Choose when ### Choose Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; mlflow is Python. - License: Made-With-ML is MIT, mlflow 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, data-science, deep-learning. - If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ### Choose mlflow if… - mlflow is primarily Python; Made-With-ML is Jupyter Notebook. - License: mlflow is Apache-2.0, Made-With-ML is MIT. - Tags unique to mlflow: agentops, agents, ai-governance, langchain. - Also covers Evaluation & Observability. - When you need a vendor-neutral solution for managing the lifecycle of AI and ML models, including debugging, evaluation, monitoring, and optimization. ## 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 mlflow - If you are looking for a platform that is tightly integrated with a specific vendor's ecosystem, as MLflow's vendor-neutral approach may not leverage vendor-specific optimizations. - When your project strictly requires proprietary features or services that are not available in open-source platforms like MLflow. - If your team prefers a more specialized tool for a specific aspect of AI/ML lifecycle management, as MLflow is a general-purpose platform and may not offer the depth of features in a niche area. ## Common questions ### What is the difference between Made-With-ML and mlflow? Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. mlflow: Open source AI engineering platform for agents, LLMs, and ML models. See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over mlflow? Choose Made-With-ML over mlflow when Made-With-ML is primarily Jupyter Notebook; mlflow is Python; License: Made-With-ML is MIT, mlflow 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, data-science, deep-learning; 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 mlflow over Made-With-ML? Choose mlflow over Made-With-ML when mlflow is primarily Python; Made-With-ML is Jupyter Notebook; License: mlflow is Apache-2.0, Made-With-ML is MIT; Tags unique to mlflow: agentops, agents, ai-governance, langchain; Also covers Evaluation & Observability; When you need a vendor-neutral solution for managing the lifecycle of AI and ML models, including debugging, evaluation, monitoring, and optimization. ### 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 mlflow? If you are looking for a platform that is tightly integrated with a specific vendor's ecosystem, as MLflow's vendor-neutral approach may not leverage vendor-specific optimizations. When your project strictly requires proprietary features or services that are not available in open-source platforms like MLflow. If your team prefers a more specialized tool for a specific aspect of AI/ML lifecycle management, as MLflow is a general-purpose platform and may not offer the depth of features in a niche area. ### Is Made-With-ML or mlflow more popular on GitHub? Made-With-ML has more GitHub stars (49,547 vs 28,010). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and mlflow open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, mlflow: Apache-2.0). ### Where can I find alternatives to Made-With-ML or mlflow? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [mlflow alternatives](/tools/mlflow-mlflow/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/alternatives.md), [mlflow markdown twin](/tools/mlflow-mlflow/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-mlflow-mlflow.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 mlflow? Made-With-ML: Slowing. mlflow: 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 mlflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [mlflow trust report](/tools/mlflow-mlflow/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/_