W&B Tutorials

The following tutorials take you through the fundamentals of W&B for machine learning experiment tracking, model evaluation, hyperparameter tuning, model and dataset versioning, and more.

1. Track experiments
2. Visualize predictions
3. Tune hyperparameters
4. Track models and datasets
5. Register models
6. Iterate on LLMs

Popular ML framework tutorials

See the following tutorials for step by step information on how to use popular ML frameworks and libraries with W&B:

• PyTorch
• PyTorch Lightning
• HuggingFace 🤗 Transformers
• Tensorflow
  • Track experiments
  • Tune hyperparameters
• Keras
  • Track experiments
  • Visualize predictions
  • Model Checkpointing
• XGBoost
  • Tune hyperparameters

Other resources

Visit the W&B AI Academy to learn how to train, fine-tune and use LLMs in your applications. Implement MLOps and LLMOps solutions. Tackle real-world ML challenges with W&B courses.

• Large Language Models (LLMs)
  • LLM Engineering: Structured Outputs
  • Building LLM-Powered Apps
  • Training and Fine-tuning Large Language Models
• Effective MLOps
  • Model CI/CD
  • Effective MLOps: Model Development
  • CI/CD for Machine Learning (GitOps)
  • Data Validation in Production ML Pipelines
  • Machine Learning for Business Decision Optimization
• W&B Models
  • W&B 101
  • W&B 201: Model Registry