Weights & Biases AI Academy

Welcome to the W&B AI Academy! This repository contains materials for learning AI, organized by topic. These materials are designed to complement our online courses but can also be useful on their own.

🤖 Large Language Models (LLMs)

Course	Instructor	Description
🆕 Developer's guide to LLM prompting	Anish Shah MLE @ W&B Teodora Danilovic Prompt Engineer @ AutogenAI	Everything you need to get started with prompt engineering, from system prompts to model-specific strategies.
🆕 RAG in production	Bharat Ramanathan MLE @ W&B Ayush Thakur MLE @ W&B Charles Pierse Head of Weaviate Labs	Practical RAG techniques for engineers: production-ready solutions to optimize performance, cut costs, and enhance accuracy.
LLM Engineering: Structured Outputs	Jason Liu Independent Consultant	Improve LLM engineering skills, learn about structured JSON output handling, function calling, and complex validations.
Building LLM-Powered Apps	Darek Kłeczek MLE @ W&B Bharat Ramanathan MLE @ W&B Thomas Capelle MLE @ W&B Shreya Rajpal Creator of Guardrails AI Anton Troynikov Co-Founder of Chroma Shahram Anver Co-Creator of Rebuff	Learn to build LLM-powered applications using LLM APIs, Langchain, and W&B LLM tooling.
Training and Fine-tuning LLMs	Darek Kłeczek MLE @ W&B Ayush Thakur MLE @ W&B Jonathan Frankle Chief Scientist @ MosaicML Weiwei Yang Principal SDE Manager @ Microsoft Research Mark Saroufim PyTorch Engineer @ Meta	Explore LLM architecture, training techniques, and fine-tuning methods, including LoRA and RLHF.
Evaluate and Debug Generative AI	Carey Phelps Founding Product Manager @ W&B	Practice evaluating and debugging Generative AI work using the W&B AI Developer Platform.

🚀 MLOps

Course	Instructor	Description
Model CI/CD	Noa Schwartz Product Manager @ W&B Darek Kłeczek MLE @ W&B Hamel Husain Founder @ Parlance Labs	Overcome model chaos, automate workflows, ensure governance, and streamline the end-to-end model lifecycle.
Effective MLOps: Model Development	Thomas Capelle MLE @ W&B Darek Kłeczek MLE @ W&B Hamel Husain Founder @ Parlance Labs	Learn to accelerate and scale model development, improve productivity, and ensure reproducibility.
CI/CD for Machine Learning (GitOps)	Hamel Husain Founder @ Parlance Labs	Streamline ML workflows using GitHub Actions and integrate W&B experiment tracking.
Data Validation in Production ML Pipelines	Shreya Shankar PhD student @ UC Berkeley	Build robust production ML pipelines, detect data drift, and manage data quality.
ML for Business Decision Optimization	Dan Becker	Optimize business decisions and translate ML predictions into actionable insights.

📊 W&B Tools

Course	Instructor	Description
W&B 101	Scott Condron MLE @ W&B	Introduction to W&B with a focus on experiment tracking, visualization, and optimization.
W&B 201: Model Registry	Ken Lee MLE @ W&B	Advanced model management using W&B for logging, registering, and managing ML models.

🌍 International Courses

Course	Language	Description
효율적인 MLOps: 모델 개발	Korean	Comprehensive program on bringing ML models to life, optimizing performance, and preparing for primetime.
効果的なMLOps: モデル開発	Japanese	Learn to accelerate and scale model development, improve productivity, and ensure reproducibility.

🏫 Resources for Educators

• Materials for Educators, TAs, and Students

🧮 Math for ML

• Math refresher for Machine Learning

For more information and to enroll in courses, visit the W&B AI Academy website.