Lilly Tech Systems
Learn Weights & Biases
Master the ML developer platform for experiment tracking, hyperparameter optimization, dataset versioning, and model management — used by top AI teams worldwide.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is W&B, its core features, and how it compares to MLflow, Neptune, and ClearML.
2. Setup & Configuration
Install wandb, authenticate, configure projects, and log your first experiment.
3. Experiment Tracking
Log metrics, parameters, media, and tables. Build custom dashboards and compare runs.
4. Sweeps
Hyperparameter optimization with Bayesian, grid, and random search strategies.
5. Artifacts & Model Registry
Version datasets and models, build lineage graphs, and use the model registry.
6. Best Practices
Team collaboration, reports, integrations with PyTorch/TF/sklearn, and production workflows.
What You'll Learn
By the end of this course, you'll be able to:
Track Everything
Log metrics, parameters, code, and artifacts for every experiment with full reproducibility.
Optimize Hyperparameters
Run distributed sweeps with Bayesian optimization to find the best model configuration.
Version Data & Models
Use W&B Artifacts to version datasets and models with full lineage tracking.
Collaborate Effectively
Share interactive reports, compare experiments across teams, and build reproducible workflows.