REPORTS
Share insights and enhance collaboration in deep learning projects
W&B Reports enable you to document findings from your machine learning experiments and share them with your team and internal stakeholders. Track results dynamically, keep everyone informed, gather feedback, and plan your next steps. Plus, you can compare results across projects and set benchmarks.
Track results dynamically
Say goodbye to screenshots and unorganized notes. Embed plots, notes, and dynamic experiments with flexible formats. Keep track of your results and plan your next research direction.
Communicate effectively
It’s never been easier to share updates and outcomes of your machine learning projects with your coworkers and internal stakeholders. Explain how your model works, show plots and visualizations of how your model versions improved, discuss bugs, and demonstrate progress towards milestones.
Gather feedback
Make live comments, describe your findings, and take snapshots of your work log.
Set benchmarks across projects
Easily compare results from two different projects and establish benchmarks that are automatically updated.
For more information see our docs
The Weights & Biases end-to-end AI developer platform
Weave
- Traces
- Debug agents and AI applications
- Evaluations
- Rigorous evaluations of agentic AI systems
- Playground
- Explore prompts and models
- Agents
- Observability tools for agentic systems
- Guardrails
- Block prompt attacks and harmful outputs
- Monitors
- Continuously improve in prod
- Models
- Experiments
- Track and visualize your ML experiments
- Sweeps
- Optimize your hyperparameters
- Tables
- Visualize and explore your ML data
- Core
- Inference
- Explore hosted, open-source LLMs
- Registry
- Publish and share your AI models and datasets
- Artifacts
- Version and manage your AI pipelines
- Reports
- Document and share your AI insights
- SDK
- Log AI experiments and artifacts at scale
- Automations
- Trigger workflows automatically
The Weights & Biases platform helps you streamline your workflow from end to end
Models
Experiments
Track and visualize your ML experiments
Sweeps
Optimize your hyperparameters
Registry
Publish and share your ML models and datasets
Automations
Trigger workflows automatically
Weave
Traces
Explore and debug LLMs
Evaluations
Rigorous evaluations of GenAI applications
Core
Artifacts
Version and manage your ML pipelines
Tables
Visualize and explore your ML data
Reports
Document and share your ML insights
SDK
Log ML experiments and artifacts at scale