This repository provides educational materials for learning Artificial Intelligence, specifically focusing on Large Language Models (LLMs) and MLOps. It targets engineers, developers, and students seeking practical knowledge and hands-on experience, complementing online courses with self-contained resources.

How It Works

The materials are organized by topic and presented in a course-like format, featuring instructors from Weights & Biases and industry experts. Each course outlines key learning objectives, practical techniques, and relevant tools, often integrating Weights & Biases platform features for experiment tracking, model management, and evaluation.

Quick Start & Requirements

• Materials are primarily accessed via the repository's README and linked external resources.
• Specific course prerequisites are detailed within individual course descriptions.
• No direct installation or execution commands are provided for the educational content itself.
• Further information and course enrollment are available on the W&B AI Academy website.

Highlighted Details

• Comprehensive coverage of LLM topics including RAG, prompt engineering, structured outputs, and fine-tuning.
• In-depth MLOps modules covering CI/CD, model development, data validation, and GitOps.
• Dedicated sections on utilizing Weights & Biases tools for experiment tracking and model registry.
• International courses available in Korean and Japanese, alongside English content.

Maintenance & Community

• Developed and maintained by Weights & Biases, with contributions from industry experts and researchers.
• Links to community resources such as Discord or Slack are not explicitly provided in the README.

Licensing & Compatibility

• The repository itself is likely licensed under a permissive open-source license, common for educational content.
• Specific tool integrations may be subject to their own licensing terms.

Limitations & Caveats

The repository serves as a curriculum and resource hub; it does not contain executable code for the courses themselves, requiring users to follow external links or integrate concepts with their own development environments.