| title |
Get Started with Weights & Biases |
| description |
Choose the right W&B product for your use case and learn how to get started |
Welcome to Weights & Biases! Before getting started with our products, it's important to identify which ones suit your use case.
| Product |
Best For |
Key Features |
| W&B Models |
Training ML models from scratch |
Experiment tracking, hyperparameter optimization, model registry, visualizations |
| W&B Weave |
Building LLM applications |
Tracing, prompt management, evaluation, cost tracking for production AI apps |
| Serverless Inference |
Using pre-trained models |
Hosted open-source models, API access, model playground for testing |
| Serverless Training |
Fine-tuning and post-training models |
Create and deploy LoRAs and custom model adaptations with reinforcement learning (RL) and supervised fine-tuning (SFT) |
| Serverless Sandboxes |
Running isolated compute environments |
On-demand, disposable sandboxes for training jobs, agent tool use, and reproducible experiments |
The "hello world" of W&B, which guides you to logging your first data.
A full-fledged tutorial that walks through the entire Models product using a real ML experiment.
A video-led course that emphasizes experiment tracking and features quizzes to ensure comprehension.
Learn how to use W&B Models to track experiments, manage artifacts, and optimize models through hands-on tutorials.
Learn how to decorate your code so that calling into an LLM logs Weave traces and sets you on the path of a perfect LLM workflow.
A full-fledged tutorial that shows Weave doing real-world evaluation of the performance of various models hosted by Serverless Inference
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A video-led course that teaches you how to log, debug, and evaluate language model workflows, and features quizzes to ensure comprehension.
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Learn how to use Weave to trace, evaluate, and debug LLM applications and how to use Weave to build production-ready LLM applications.
Features a quickstart that shows how you use the standard OpenAI REST API to call any model hosted on Serverless Inference.
A full-fledged tutorial that shows Weave doing real-world evaluation of the performance of various models hosted by Serverless Inference
Serverless Inference is really simple to use. Click on any model we host, start trying prompts, and see our observability layer kick into action.
Run through a few quick examples of Serverless Inference tracing calls to popular LLMs and evaluating the results.
Use Serverless RL with OpenPipe's ART library to train a model to play the game 2048.
After creating your trained model, learn how to use it in your code.
Learn what Serverless Sandboxes are and when to use them for isolated, disposable compute.
Spin up your first sandbox in Python and start running code in seconds.
A full tutorial that walks through training a PyTorch model inside a sandbox from start to finish.
Give an OpenAI agent access to a sandbox for safe, isolated tool use.