# The Full Stack (fullstackdeeplearning.com) vs Machine learning at scale

The Full Stack (fullstackdeeplearning.com) records 46,082 monthly visits with +6.6% month-over-month growth and Free (educational content) pricing. Machine learning at scale records 0 monthly visits with 0% growth and Freemium pricing.

| Metric | [The Full Stack (fullstackdeeplearning.com)](https://openaitools.app/tool/fullstackdeeplearning-com) | [Machine learning at scale](https://openaitools.app/tool/machine-learning-at-scale) |
| :--- | :--- | :--- |
| Website URL | https://fullstackdeeplearning.com | https://machinelearningatscale.com |
| Categories | AI Course, Large Language Models (LLMs), Prompt Engineering, AI Developer Tools | AI Tutorial, AI Course, Large Language Models (LLMs), AI Developer Tools |
| Pricing Model | Free (educational content) | Freemium |
| Monthly Visits | 46,082 | 0 |
| MoM Growth Rate | +6.6% | 0% |
| Primary Market | United States | Global |
| Direct Visits | 26,064 | 0 |
| Review Score | 4.6 | 4.6 |
| Saved Count | 1 | 2 |
| Supported Platforms | Website | Website |

## The Full Stack (fullstackdeeplearning.com)

Full Stack Deep Learning began as a course teaching engineers to take deep learning from notebook to production, and it has grown into a broader educational hub. The site hosts complete course materials — lecture pages, slides, lab notebooks, and video walkthroughs — for both its Deep Learning Course and its LLM Bootcamp, so learners can follow along at their own pace without paying for a seat.

The curriculum is deliberately full-stack rather than model-centric. The Deep Learning Course covers development infrastructure, experiment management, troubleshooting and testing, data management and annotation, deployment, continual learning, and model monitoring, plus team and project management and ethics. The LLM Bootcamp goes into prompt engineering, augmented language models, LLMOps, and user experience for language interfaces, and includes build-along project walkthroughs.

Beyond courses, the site publishes a blog, hosts a manifesto on building AI products collaboratively, and maintains reference material such as a cloud GPU pricing guide. Because the materials are released publicly and remain accessible, the site functions as a long-lived library rather than a time-boxed bootcamp, and its audience spans individual developers, teams, and educators looking for structured material.

**Pricing & Plans:** The published course material on fullstackdeeplearning.com is available free of charge. There is no conventional subscription or per-seat pricing listed on the site; the project has historically been supported by sponsors and community interest. Any future paid programs would be announced on the site.

- **Free LLM Bootcamp materials:** Lecture pages and resources covering prompt engineering, augmented language models, LLMOps, and language UI/UX.
- **Full Deep Learning Course archive:** Multi-year course content on infrastructure, data management, deployment, monitoring, and ML team organization.
- **Working with AI Agents:** Current material on collaborating with and building agent-based systems, including a perspective on multiplayer AI.
- **Cloud GPU reference:** A maintained guide to cloud GPU options that helps teams choose compute for training and inference.
- **Blog and community resources:** Articles and updates aimed at practitioners building AI products, with a newsletter to follow new releases.

**Strengths:** Course materials are open and free to follow at your own pace; Covers the full product lifecycle, not just model training; Maintained across multiple years, so it documents how practice evolved; Includes hands-on labs and build-along project walkthroughs

**Limitations:** Some flagship courses date from 2021-2023, so tooling specifics age; Not an interactive cohort programme, so there is no instructor feedback; Sparse on formal certification or structured assessments

[Profile (Markdown)](https://openaitools.app/tool/fullstackdeeplearning-com.md)

## Machine learning at scale

Machine Learning at Scale is a newsletter and learning resource published by Ludo, a machine learning engineer at Google. Its stated goal is to help readers become '10x' ML engineers by going beyond introductory tutorials and into the realities of running machine learning in production: how systems are designed, where they break, and which patterns scale.

The content programme is organised around case studies, design patterns, deep dives and career advice. Topics include LLM inference at scale, recommendation systems, search and ranking, RAG in the AI era, and ads systems, alongside coming-soon deep dives into machine learning systems. The author draws on experience fighting abuse at 500k QPS, pretraining and fine-tuning transformer models, working on end-to-end YouTube Ads systems, applying ML at CERN, and a computer-vision thesis at Volvo.

Community and career support are part of the offering: a growing base of ML engineers from leading companies subscribe weekly, and the site includes an FAQ on getting started with ML, getting into big tech, MLE versus data scientist roles, and whether a PhD is necessary. Extra resources include an upcoming MLSys course, a YouTube channel and curated book recommendations.

**Pricing & Plans:** Freemium. The newsletter and much of the resource library (articles, FAQ, career advice) are free to read, with email subscription; the site also references paid offerings such as a course.

- **Case studies and design patterns:** Read breakdowns of ML systems design, LLM inference at scale, recommendation systems, search and ranking, ads systems, and RAG.
- **Weekly newsletter:** Get weekly high-quality insights and practical guidance written by an ML engineer working on large-scale production systems.
- **ML systems deep dives:** Deep dives into LLM optimisation, LLM training and ML system design for engineers moving beyond tutorials.
- **ML career advice and FAQ:** Guidance on getting started in ML, breaking into big tech, choosing between MLE and data scientist paths, and whether a PhD is worth it.
- **Course, YouTube channel and book picks:** Supplement your reading with a dedicated MLSys course, a YouTube channel, curated book recommendations, and ML jobs listings.

**Strengths:** Written by a practising ML engineer at Google; Focuses on production ML systems rather than toy examples; Free weekly newsletter with a large readership of ML engineers; Practical career guidance on breaking into big tech and MLE roles

[Profile (Markdown)](https://openaitools.app/tool/machine-learning-at-scale.md)

## Frequently Asked Questions

### How do The Full Stack (fullstackdeeplearning.com) and Machine learning at scale compare in monthly traffic?

The Full Stack (fullstackdeeplearning.com) has 46,082 monthly visits, while Machine learning at scale has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

The Full Stack (fullstackdeeplearning.com) has a month-over-month growth rate of +6.6%; Machine learning at scale has 0%.

### How does pricing compare?

The Full Stack (fullstackdeeplearning.com): Free (educational content). The published course material on fullstackdeeplearning.com is available free of charge. There is no conventional subscription or per-seat pricing listed on the site; the project has historically been supported by sponsors and community interest. Any future paid programs would be announced on the site. Machine learning at scale: Freemium. Freemium. The newsletter and much of the resource library (articles, FAQ, career advice) are free to read, with email subscription; the site also references paid offerings such as a course.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [DeepLearning.AI](https://openaitools.app/tool/deeplearning-ai) | 2,562,512 | -7.2% | Freemium | AI engineering courses, certificates, and the world's biggest AI newsletter | [.md](https://openaitools.app/tool/deeplearning-ai.md) |
| 2 | [The Rundown AI](https://openaitools.app/tool/the-rundown-ai) | 1,018,070 | +28.6% | Free | The top AI news and how to apply it, in five minutes a day | [.md](https://openaitools.app/tool/the-rundown-ai.md) |
| 3 | [OpenAI Academy](https://openaitools.app/tool/openai-academy) | 937,379 | -6.3% | Free | Free courses, live events and community for building real AI skills | [.md](https://openaitools.app/tool/openai-academy.md) |
| 4 | [LabLab.ai](https://openaitools.app/tool/lablab-ai) | 431,529 | -39.7% | Free | Free AI hackathons and a global community for AI builders | [.md](https://openaitools.app/tool/lablab-ai.md) |
| 5 | [fast.ai](https://openaitools.app/tool/fast-ai) | 250,720 | -10.4% | Free | Free practical deep learning courses, research and open-source libraries | [.md](https://openaitools.app/tool/fast-ai.md) |
| 6 | [Learn Prompting](https://openaitools.app/tool/learn-prompting) | 159,807 | -8.6% | Freemium, Free Trial, Contact for Pricing | Free open course for prompt engineering and AI security | [.md](https://openaitools.app/tool/learn-prompting.md) |
| 7 | [Open Data Science (ODS.ai)](https://openaitools.app/tool/ods-ai) | 159,630 | +15.9% | Free | Community hub for data science, machine learning and AI practitioners | [.md](https://openaitools.app/tool/ods-ai.md) |
| 8 | [AIToolGo](https://openaitools.app/tool/aitoolgo) | 85,066 | +11.8% | Free | Discover AI tools and learn AI fundamentals in one searchable hub | [.md](https://openaitools.app/tool/aitoolgo.md) |
| 9 | [AI SDK Agents](https://openaitools.app/tool/ai-sdk-agents) | 26,211 | -26.2% | Freemium | Full-stack Vercel AI SDK patterns for shipping real AI apps | [.md](https://openaitools.app/tool/ai-sdk-agents.md) |
| 10 | [Creators' AI](https://openaitools.app/tool/creators-ai) | 20,308 | -22.1% | Paid, Contact for Pricing | An AI newsletter with curated tools, news and guides for creators and founders | [.md](https://openaitools.app/tool/creators-ai.md) |

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Source: https://openaitools.app/compare/fullstackdeeplearning-com-vs-machine-learning-at-scale

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