---
title: "AI-Engineering.academy vs Made-With-ML"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/adithya-s-k-ai-engineering-academy-vs-gokumohandas-made-with-ml"
tools: ["adithya-s-k-ai-engineering-academy", "gokumohandas-made-with-ml"]
---

# AI-Engineering.academy vs Made-With-ML

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick AI-Engineering.academy if aI-Engineering.academy is an educational content repository specialized in the practical application of AI concepts using Jupyter Notebooks. It's ideal for learning about fine-tuning and serving large language models; 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.

[AI-Engineering.academy](https://aiengineering.academy) reports 2.4k GitHub stars, 280 forks, and 9 open issues, last pushed Feb 27, 2026. [Made-With-ML](https://madewithml.com) has 50k stars, 7.8k forks, and 25 open issues, last pushed Mar 4, 2026. Figures are from public GitHub metadata via [AI-Engineering.academy's repository](https://github.com/adithya-s-k/AI-Engineering.academy) and [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML).

| | [AI-Engineering.academy](/tools/adithya-s-k-ai-engineering-academy.md) | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) |
| --- | --- | --- |
| Tagline | Mastering Applied AI, One Concept at a Time | Learn to develop, deploy and iterate on production-grade ML applications |
| Stars | 2,383 | 49,547 |
| Forks | 280 | 7,778 |
| Open issues | 9 | 25 |
| Language | Jupyter Notebook | Jupyter Notebook |
| Adopt for | AI-Engineering.academy is an educational content repository specialized in the practical application of AI concepts using Jupyter Notebooks. It's ideal for learning about fine-tuning and serving large language models. | 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. |
| Persona | - | - |
| Runtime | - | - |
| License | Available under MIT license, allowing broad usage with attributions | MIT |
| Categories | Inference & Serving, LLM Frameworks, Model Training | Developer Tools, Inference & Serving, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [AI-Engineering.academy](/tools/adithya-s-k-ai-engineering-academy.md) | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) |
| --- | --- | --- |
| Days since push | 203d | 199d |
| Open issues (now) | 9 | 25 |
| Stars delta | +20 (30d) | +473 (30d) |
| Open issues delta | +2 (30d) | -1 (30d) |
| Full report | [trust report](/tools/adithya-s-k-ai-engineering-academy/trust.md) | [trust report](/tools/gokumohandas-made-with-ml/trust.md) |

## Decision facts: AI-Engineering.academy

- **Hosting:** self hosted - The content is accessible directly through Jupyter Notebooks and does not require the setup of a separate server or environment.
- **Pricing:** freemium - Currently freely available, but as more features are added, some advanced modules might be behind a paywall.
- **Adopt for:** AI-Engineering.academy is an educational content repository specialized in the practical application of AI concepts using Jupyter Notebooks. It's ideal for learning about fine-tuning and serving large language models.
- **License detail:** Available under MIT license, allowing broad usage with attributions

## 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.

## Choose when

### Choose AI-Engineering.academy if…

- The content is accessible directly through Jupyter Notebooks and does not require the setup of a separate server or environment.
- Pricing: Currently freely available, but as more features are added, some advanced modules might be behind a paywall..
- Tags unique to AI-Engineering.academy: fine-tuning, inference, large-language-models, quantization.
- Also covers LLM Frameworks.
- - When you need hands-on, guided tutorials to understand how to fine-tune large language models with a focus on practical applications.

### 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.
- 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 NOT to use AI-Engineering.academy

- - Avoid this resource if you are seeking theoretical deep-dive content without practical applications; the focus here is on hands-on learning.
- - If your goal is to explore a wide range of AI-related topics beyond language models and inference, as this repository specializes narrowly in these areas.
- - Not suitable for individuals needing real-time personalized guidance from experts but rather prefer pre-crafted educational materials.

## 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.

## Common questions

### What is the difference between AI-Engineering.academy and Made-With-ML?

AI-Engineering.academy: Mastering Applied AI, One Concept at a Time. Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. See the comparison table for live GitHub stats and shared categories.

### When should I choose AI-Engineering.academy over Made-With-ML?

Choose AI-Engineering.academy over Made-With-ML when The content is accessible directly through Jupyter Notebooks and does not require the setup of a separate server or environment; Pricing: Currently freely available, but as more features are added, some advanced modules might be behind a paywall.; Tags unique to AI-Engineering.academy: fine-tuning, inference, large-language-models, quantization; Also covers LLM Frameworks; - When you need hands-on, guided tutorials to understand how to fine-tune large language models with a focus on practical applications.

### When should I choose Made-With-ML over AI-Engineering.academy?

Choose Made-With-ML over AI-Engineering.academy 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; 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 avoid AI-Engineering.academy?

- Avoid this resource if you are seeking theoretical deep-dive content without practical applications; the focus here is on hands-on learning. - If your goal is to explore a wide range of AI-related topics beyond language models and inference, as this repository specializes narrowly in these areas. - Not suitable for individuals needing real-time personalized guidance from experts but rather prefer pre-crafted educational materials.

### 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.

### Is AI-Engineering.academy or Made-With-ML more popular on GitHub?

Made-With-ML has more GitHub stars (49,547 vs 2,383). Stars measure visibility, not whether either tool fits your constraints.

### Are AI-Engineering.academy and Made-With-ML open source?

Yes - both are open-source projects on GitHub (AI-Engineering.academy: MIT, Made-With-ML: MIT).

### Where can I find alternatives to AI-Engineering.academy or Made-With-ML?

GraphCanon lists graph-backed alternatives at [AI-Engineering.academy alternatives](/tools/adithya-s-k-ai-engineering-academy/alternatives) and [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) ([AI-Engineering.academy markdown twin](/tools/adithya-s-k-ai-engineering-academy/alternatives.md), [Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/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/adithya-s-k-ai-engineering-academy-vs-gokumohandas-made-with-ml.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, AI-Engineering.academy or Made-With-ML?

AI-Engineering.academy: Slowing. Made-With-ML: Slowing. 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 AI-Engineering.academy and Made-With-ML?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AI-Engineering.academy trust report](/tools/adithya-s-k-ai-engineering-academy/trust); [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=adithya-s-k-ai-engineering-academy`](/api/graphcanon/graph?tool=adithya-s-k-ai-engineering-academy)
- 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/_