LLM app-building curriculum
The LLM Bootcamp covers prompt engineering, augmented language models, UX for language user interfaces, LLMOps, and a one-hour app launch walkthrough.
The Full Stack
RivendicaThe Full Stack offers free courses, bootcamps, and community resources for building AI-powered products, from prompt or model selection to deployment and improvement.
Overview
The Full Stack is a site for news, community, and courses for people building AI-powered products. Its materials focus on the practical work of taking an AI idea from model selection or prompt design through production deployment, user experience, and continual improvement.
The site’s main course offerings are the Deep Learning Course and the LLM Bootcamp. The Deep Learning Course is aimed at people who have already trained one or more models and want to build AI-powered applications from the ground up, while the LLM Bootcamp focuses on best practices and tools for LLM-powered apps and is presented as a free set of recorded lectures and course materials.
What you can learn
Full lifecycle ML topics
The Deep Learning course covers the workflow around model development, including infrastructure, experiment management, troubleshooting, data management, deployment, monitoring, and ethics.
Multiple course iterations
The course archive includes multiple years and formats, such as online cohorts, Berkeley bootcamps, and university-hosted courses at UC Berkeley and the University of Washington.
Project walkthroughs and tooling examples
The Spring 2023 LLM Bootcamp includes a project walkthrough for askFSDL, with tooling for Python projects, ETL/data processing, deployment on Modal, and monitoring with Gantry.
Supplementary talks and panels
The LLM Bootcamp also includes invited talks on topics such as training your own LLM, agents, and OpenAI product learnings, adding a broader industry perspective.
Common ways people use it
Getting started with LLM apps
Use the LLM Bootcamp to learn practical steps for building an LLM app, from prompt engineering and model choice to UX and production rollout.
Moving from model training to product work
Use the Deep Learning Course when you already know the basics and want a broader workflow for taking a model into an application with deployment, monitoring, and iteration.
Reviewing past course material
Use the archived course iterations to follow a structured curriculum from previous years, including online cohorts and university-hosted versions.
Studying practical implementation examples
Use the project walkthroughs and lecture materials as references for building a real application, including data processing, deployment, and monitoring patterns.
Learning from expert-led sessions
Use the talks and lectures to compare approaches to LLMs, agents, and production concerns across different instructors and industry perspectives.
Pros and Cons
Pros
- Covers the full lifecycle of AI-powered product work, not just model training.
- Combines technical topics with deployment, UX, and operational concerns.
- Offers multiple formats, including online, bootcamp, and university course iterations.
- Materials are presented as free to access in the source text.
- Includes practical lectures, walkthroughs, and project-oriented content.
Cons
- The source does not provide a clear public pricing table or enrollment model for every course iteration.
- Some capability details are tied to specific bootcamp sessions and archived course materials rather than a single standardized product page.
FAQ
Is The Full Stack free to access?
The Full Stack offers free courses and bootcamp materials focused on building AI-powered products, including deep learning and LLM apps. The course site says the materials are available for free and covers recorded lectures and past iterations.
Who is the course content for?
The materials are aimed at people building AI-powered products. The LLM Bootcamp says its lectures are meant to get people with Python experience ready to start building LLM applications, and that machine learning, frontend, or backend experience is helpful.
What format do the offerings use?
The site describes a mix of online courses, in-person bootcamps, and official university courses. The LLM Bootcamp materials include recorded lectures, and the course page notes past online and university-hosted iterations.
What background do I need before starting?
The LLM Bootcamp page says the lectures can get someone with Python experience ready to start building LLM applications, while experience in machine learning, frontend, or backend is helpful. The Deep Learning course is framed as a next step for people who have already trained at least one model.
How much does it cost?
The pricing page lists course collections and iterations, but the provided source does not show a clear pricing structure, plan limits, or checkout details. The available text mainly emphasizes that the materials are shared for free.
Quick Facts
- Category
- AI Education
- Primary format
- Online courses, bootcamps, and archived course materials
- Audience
- People building AI-powered products
- Source domain
- fullstackdeeplearning.com
- Notable offerings
- Deep Learning Course and LLM Bootcamp
- Availability
- Free materials mentioned in source text