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 |
|---|---|---|
| 🆕 RAG++ | Bharat Ramanathan | |
| MLE @ W&B |
Ayush Thakur
MLE @ W&B
Meor Amer
Developer Advocate @ Cohere
Charles Pierse
Head of Weaviate Labs | Practical RAG techniques for engineers: production-ready solutions to optimize performance, cut costs, and enhance accuracy. |
| 🆕 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. |
| 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
🧮 Math for ML
For more information and to enroll in courses, visit the W&B AI Academy website.