To begin using Weights & Biases, first install the SDK by running "pip install wandb" in your environment. Once installed, you will need to authenticate your machine or notebook. Navigate to wandb.ai/settings in your web browser to create an API key. Copy this key and use it to log in when prompted, or set it as an environment variable. After successful installation and login, initialize a W&B run within your Python script or notebook by adding "wandb.init()" at the start of your experiment code. This command establishes a connection to the W&B platform and begins tracking your experiment. You have explicit control over what data is logged; use specific W&B API calls like "wandb.run.log()" to record datasets, plots, tables, and even PyTorch gradients. For team collaboration, create a new team within the W&B interface and invite members to log their runs to the same project. This enables side-by-side comparison of different models and experiments. If you're working in an environment without immediate internet access, W&B supports offline logging, allowing you to save metrics locally and sync them to the platform later when connectivity is restored. When developing LLM-powered applications with W&B Weave, the toolkit automatically captures crucial information such as client details, timing, token usage, user and run context, derived cost estimates, and a representation of the Op's source code for debugging and versioning. You retain fine-grained control over the data captured by Weave. Note that artifacts logged anonymously cannot be claimed and will expire after seven days. For any questions or assistance, Weights & Biases Customer Support is available at support@wandb.com.