--- title: "Made-With-ML vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-visenger-awesome-mlops" tools: ["gokumohandas-made-with-ml", "visenger-awesome-mlops"] --- # Made-With-ML vs awesome-mlops *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 awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. [Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 26 open issues, last pushed Mar 4, 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 [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | Learn to develop, deploy and iterate on production-grade ML applications | A curated list of references for MLOps | | Stars | 49,074 | 14,127 | | Forks | 7,710 | 2,101 | | Open issues | 26 | 44 | | Language | Jupyter Notebook | - | | 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. | awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | 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) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 162d | 621d | | Open issues (now) | 26 | 44 | | Stars delta | +371 (30d) | Unknown | | Open issues delta | -1 (30d) | Unknown | | Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/visenger-awesome-mlops/trust.md) | **Typed relationship:** Made-With-ML _(related)_ awesome-mlops Both Made-With-ML and this awesome list provide references on MLOps tools for deploying, monitoring, and scaling machine learning applications. ## Shared compatibility - **Python**: [Made-With-ML](/tools/gokumohandas-made-with-ml.md) - Python runtime; [awesome-mlops](/tools/visenger-awesome-mlops.md) - Python runtime ## 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: awesome-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## Choose when ### Choose Made-With-ML if… - Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.. - Both Made-With-ML and this awesome list provide references on MLOps tools for deploying, monitoring, and scaling machine learning applications. - 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 awesome-mlops if… - Both Made-With-ML and this awesome list provide references on MLOps tools for deploying, monitoring, and scaling machine learning applications. - 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. ## 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 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 Made-With-ML and awesome-mlops? Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over awesome-mlops? Choose Made-With-ML over awesome-mlops when Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.; Both Made-With-ML and this awesome list provide references on MLOps tools for deploying, monitoring, and scaling machine learning applications; 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 awesome-mlops over Made-With-ML? Choose awesome-mlops over Made-With-ML when Both Made-With-ML and this awesome list provide references on MLOps tools for deploying, monitoring, and scaling machine learning applications; 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. ### 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 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 Made-With-ML or awesome-mlops more popular on GitHub? Made-With-ML has more GitHub stars (49,074 vs 14,127). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Made-With-ML or awesome-mlops? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/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/gokumohandas-made-with-ml-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, Made-With-ML or awesome-mlops? Made-With-ML: Slowing. 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 Made-With-ML and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/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/_