01 What is it?
Weights & Biases (W&B) provides experiment tracking, evaluations, model registry and Weave for LLM observability. It is the standard tool for ML teams that need rigorous experiment management, and it is rapidly extending into the LLM and agent observability space.
02 Why implement it?
- Experiment tracking with full lineage and reproducibility
- Model registry with promotion and approval workflows
- Weave for prompt and agent observability
- Native integrations with PyTorch, JAX, Hugging Face, LangChain
- Strong governance for regulated ML pipelines
03 How I help
I integrate W&B into your ML and agent stack, design the experiment and model lifecycle, configure the registry promotion workflow, and connect the platform to your audit and SIEM tooling.
04 Expected deliverables
- W&B integration into your ML and agent stack
- Experiment and model lifecycle design
- Registry promotion and approval workflow
- Audit and SIEM integration
- Operating model and team enablement