--- title: "Made-With-ML vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-rohitg00-ai-engineering-from-scratch" tools: ["gokumohandas-made-with-ml", "rohitg00-ai-engineering-from-scratch"] --- # Made-With-ML vs ai-engineering-from-scratch *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-engineering-from-scratch if ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and. [Made-With-ML](https://madewithml.com) reports 50k GitHub stars, 7.8k forks, and 25 open issues, last pushed Mar 4, 2026. [ai-engineering-from-scratch](https://aiengineeringfromscratch.com) has 55k stars, 9.6k forks, and 114 open issues, last pushed Sep 7, 2026. Figures are from public GitHub metadata via [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | Learn to develop, deploy and iterate on production-grade ML applications | Learn, build, and deploy AI engineering skills from scratch. | | Stars | 49,547 | 54,935 | | Forks | 7,778 | 9,649 | | Open issues | 25 | 114 | | 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. | ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Developer Tools, Inference & Serving, Model Training | AI Agents, Computer Vision, Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 199d | 10d | | Open issues (now) | 25 | 114 | | Stars delta | +473 (30d) | +8.1k (30d) | | Open issues delta | -1 (30d) | +7 (30d) | | Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/rohitg00-ai-engineering-from-scratch/trust.md) | ## Shared compatibility - **Python**: [Made-With-ML](/tools/gokumohandas-made-with-ml.md) - Python runtime; [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.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: ai-engineering-from-scratch - **Adopt for:** ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and ## Choose when ### Choose Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; ai-engineering-from-scratch is Python. - 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, distributed-ml. - Also covers Inference & Serving. - If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ### Choose ai-engineering-from-scratch if… - ai-engineering-from-scratch is primarily Python; Made-With-ML is Jupyter Notebook. - Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, from-scratch. - Also covers AI Agents, Computer Vision. - when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning. ## 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-engineering-from-scratch - if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. - when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. - if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. - when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments. ## Common questions ### What is the difference between Made-With-ML and ai-engineering-from-scratch? Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. ai-engineering-from-scratch: Learn, build, and deploy AI engineering skills from scratch.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over ai-engineering-from-scratch? Choose Made-With-ML over ai-engineering-from-scratch when Made-With-ML is primarily Jupyter Notebook; ai-engineering-from-scratch is Python; 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, distributed-ml; Also covers Inference & Serving; 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-engineering-from-scratch over Made-With-ML? Choose ai-engineering-from-scratch over Made-With-ML when ai-engineering-from-scratch is primarily Python; Made-With-ML is Jupyter Notebook; Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, from-scratch; Also covers AI Agents, Computer Vision; when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning. ### 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-engineering-from-scratch? if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments. ### Is Made-With-ML or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (54,935 vs 49,547). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to Made-With-ML or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/alternatives.md), [ai-engineering-from-scratch markdown twin](/tools/rohitg00-ai-engineering-from-scratch/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-rohitg00-ai-engineering-from-scratch.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-engineering-from-scratch? Made-With-ML: Slowing. ai-engineering-from-scratch: 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 ai-engineering-from-scratch? 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-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/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/_