--- title: "Made-With-ML vs AI-Infra-from-Zero-to-Hero" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-huaizhengzhang-ai-infra-from-zero-to-hero" tools: ["gokumohandas-made-with-ml", "huaizhengzhang-ai-infra-from-zero-to-hero"] --- # Made-With-ML vs AI-Infra-from-Zero-to-Hero *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 AI-Infra-from-Zero-to-Hero if a curated resource list for AI system design focusing on large language models and various system aspects. [Made-With-ML](https://madewithml.com) reports 50k GitHub stars, 7.8k forks, and 25 open issues, last pushed Mar 4, 2026. [AI-Infra-from-Zero-to-Hero](https://huaizheng.xyz/) has 4.3k stars, 409 forks, and 14 open issues, last pushed Jul 25, 2025. Figures are from public GitHub metadata via [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [AI-Infra-from-Zero-to-Hero's repository](https://github.com/HuaizhengZhang/AI-Infra-from-Zero-to-Hero). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [AI-Infra-from-Zero-to-Hero](/tools/huaizhengzhang-ai-infra-from-zero-to-hero.md) | | --- | --- | --- | | Tagline | Learn to develop, deploy and iterate on production-grade ML applications | Awesome System for Machine Learning and LLM Infra | | Stars | 49,547 | 4,285 | | Forks | 7,778 | 409 | | Open issues | 25 | 14 | | 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. | A curated resource list for AI system design focusing on large language models and various system aspects. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Developer Tools, Inference & Serving, Model Training | Developer Tools, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [AI-Infra-from-Zero-to-Hero](/tools/huaizhengzhang-ai-infra-from-zero-to-hero.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 199d | 388d | | Open issues (now) | 25 | 14 | | Stars delta | +473 (30d) | +87 (30d) | | Open issues delta | -1 (30d) | 0 (30d) | | Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/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: AI-Infra-from-Zero-to-Hero - **Adopt for:** A curated resource list for AI system design focusing on large language models and various system aspects. ## Choose when ### Choose Made-With-ML if… - 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 AI-Infra-from-Zero-to-Hero if… - Tags unique to AI-Infra-from-Zero-to-Hero: ai-infra, genai, large-language-models, llmsys. - Also covers LLM Frameworks. - When you are aiming to understand the foundational research papers, industry practices, video tutorials specific to ML systems and LLM infrastructures without requiring implementation details. ## 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 AI-Infra-from-Zero-to-Hero - If you need step-by-step implementations for AI infrastructure setup as the repository focuses on resources rather than detailed technical instructions. - Avoid if seeking guidance specifically for real-time system deployment and tuning, since it does not cover operational tactics in depth. ## Common questions ### What is the difference between Made-With-ML and AI-Infra-from-Zero-to-Hero? Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. AI-Infra-from-Zero-to-Hero: Awesome System for Machine Learning and LLM Infra. See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over AI-Infra-from-Zero-to-Hero? Choose Made-With-ML over AI-Infra-from-Zero-to-Hero when 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 AI-Infra-from-Zero-to-Hero over Made-With-ML? Choose AI-Infra-from-Zero-to-Hero over Made-With-ML when Tags unique to AI-Infra-from-Zero-to-Hero: ai-infra, genai, large-language-models, llmsys; Also covers LLM Frameworks; When you are aiming to understand the foundational research papers, industry practices, video tutorials specific to ML systems and LLM infrastructures without requiring implementation details. ### 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 AI-Infra-from-Zero-to-Hero? If you need step-by-step implementations for AI infrastructure setup as the repository focuses on resources rather than detailed technical instructions. Avoid if seeking guidance specifically for real-time system deployment and tuning, since it does not cover operational tactics in depth. ### Is Made-With-ML or AI-Infra-from-Zero-to-Hero more popular on GitHub? Made-With-ML has more GitHub stars (49,547 vs 4,285). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and AI-Infra-from-Zero-to-Hero open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, AI-Infra-from-Zero-to-Hero: MIT). ### Where can I find alternatives to Made-With-ML or AI-Infra-from-Zero-to-Hero? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [AI-Infra-from-Zero-to-Hero alternatives](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/alternatives.md), [AI-Infra-from-Zero-to-Hero markdown twin](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/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-huaizhengzhang-ai-infra-from-zero-to-hero.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 AI-Infra-from-Zero-to-Hero? Made-With-ML: Slowing. AI-Infra-from-Zero-to-Hero: 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 AI-Infra-from-Zero-to-Hero? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [AI-Infra-from-Zero-to-Hero trust report](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/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/_