Summary

This repository provides materials for a 4-week course focused on deploying Generative AI and Agentic AI at scale. It is designed for engineers and researchers aiming to gain practical, production-ready skills in AI deployment, offering a structured curriculum to tackle complex challenges.

How It Works

The course content is organized into weekly and daily modules, with week1/day1.md serving as the starting point. The pedagogical approach emphasizes hands-on learning through overcoming "roadblocks," encouraging students to embrace infrastructure challenges as learning opportunities. It advocates for the cautious use of LLMs, stressing the importance of verifying their suggestions and fostering a supportive community.

Quick Start & Requirements

To begin, navigate to the week1 folder and open day1.md. Frequent git pull operations are advised to obtain the latest updates. While the README does not detail specific software installations, the course context implies a need for standard development environments and potentially cloud infrastructure knowledge. Links for community engagement and instructor support are available via LinkedIn and X.

Highlighted Details

The primary objective is to enable participants to deploy "Gen AI and Agentic AI at Scale in 4 weeks." The repository actively promotes community involvement through a structured pull request process for the community_contributions folder, where students can share their projects and experiences.

Maintenance & Community

The project is overseen by instructor Ed Donner, who encourages direct communication via email and community interaction on LinkedIn and X. Updates are frequent, necessitating regular git pull commands. A clear process is established for submitting community contributions via pull requests.

Licensing & Compatibility

The provided README does not specify an open-source license for the repository's content.

Limitations & Caveats

This repository contains educational materials rather than a ready-to-deploy application. Participants should expect to encounter significant "infrastructure roadblocks" and "gnarly moments," requiring persistence and self-directed research. The instructor's ability to resolve issues is dependent on their reproducibility.