The Full Stack

The Full Stack (formerly Full Stack Deep Learning, or FSDL) is a free education platform for "people building AI products," emphasizing taking an AI idea from topic selection all the way to launch and continuous iteration. The main deep learning course contains 9 lecture videos, 8 labs, and a through-line text recognition project, covering development infrastructure, troubleshooting and testing, data management, web deployment, continual learning and model monitoring, foundation models, team management, and ethics; the companion LLM Bootcamp (2023) focuses on prompt engineering, LLMOps, and rapidly shipping LLM applications. All lectures and lab materials are permanently free, the GitHub open-source ecosystem is complete (multiple repositories with thousands of stars), and there is a newsletter, a Discord community, and the open-source askFSDL RAG Q&A tool. The teaching team consists of UC Berkeley PhDs including a former OpenAI researcher and a Gradescope co-founder, and the course has been offered as a formal class at Berkeley and the University of Washington. It suits engineers, data scientists, and AI product teams with existing ML foundations who want to close their productionization gap; it does not suit total beginners or certificate seekers.

Core Features

• End-to-end AI engineering course: 9 lecture videos + 8 labs covering the full post-training lifecycle
• Through-line hands-on project: a text recognition app from data management to deployment and monitoring
• Full Stack LLM Bootcamp: prompt engineering and LLMOps in practice
• Completely free and open source: core materials free forever, multiple GitHub repositories with thousands of stars
• Authoritative instructors: a former OpenAI researcher and UC Berkeley PhDs
• Community ecosystem: newsletter, Discord, and the open-source askFSDL RAG Q&A tool

Pros

• Scarce positioning: focuses on the productionization engineering chain "after model training," filling a gap most MOOCs leave open
• Free with no marketing: core content permanently free, recognized on DataCamp's best AI courses lists
• Authoritative endorsement: a formal course at Berkeley/UW, with Karpathy publicly praising the LLM Bootcamp
• Practice-oriented: real industrial toolchains (W&B, PyTorch Lightning, serverless deployment)

Cons

• Main course content is stuck in 2022; LLM/agent toolchain coverage lags
• Free version has no certificate, grading, or Q&A; relies entirely on self-discipline, with a high drop-off rate
• High entry bar: requires at least a year of Python and deep learning foundation, not for beginners
• Opaque pricing: no standalone pricing page on the site; paid courses require inquiry-based enrollment