How to Use Weights & Biases
A beginner-friendly guide to getting started with Weights & Biases in 2026.
Getting Started: Step by Step
Create your Weights & Biases account
Visit Weights & Biases's website and sign up for a free account. You'll need an email address to get started.
Set up your workspace
Once signed in, configure your Weights & Biases workspace. Set your preferences, invite team members if needed, and customize the interface to match your workflow.
Explore experiment-tracking
One of Weights & Biases's key features is experiment-tracking. Navigate to this feature and experiment with it to understand how it fits into your workflow.
Explore model-registry
One of Weights & Biases's key features is model-registry. Navigate to this feature and experiment with it to understand how it fits into your workflow.
Explore sweeps
One of Weights & Biases's key features is sweeps. Navigate to this feature and experiment with it to understand how it fits into your workflow.
Integrate with your existing tools
Connect Weights & Biases with the other tools you use daily. Most integrations can be set up in the settings or integrations panel.
Start using it for real work
Now that you're set up, start using Weights & Biases for actual tasks. The best way to learn is by doing — don't worry about getting everything perfect right away.
Pro Tips
- Start with the free plan or trial to explore Weights & Biases's capabilities before committing to a paid subscription.
- Use keyboard shortcuts to speed up your workflow — most tools have extensive shortcut systems.
- Check Weights & Biases's official documentation and community forums for advanced tips and best practices.
- Review your workflow after 2 weeks of use and adjust your setup based on what's working and what isn't.
Key Features to Explore
Alternatives to Consider
If Weights & Biases isn't the right fit, here are some similar tools:
Ready to Try Weights & Biases?
ML experiment tracking and model management platform that helps teams log, visualize, and compare experiments for reproducible machine learning.