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Platform
Models >
Experiments
Track and visualize your ML experiments
Sweeps
Optimize your hyperparameters
Tables
Visualize and explore your ML data
Reports
Visualize and explore your ML data
Training >
Serverless RL
Fine-tune LLMs without managing GPUs
ART
Open-source RL framework
Ruler
Automated reward function for RL
Inference >
OpenAI OSS
GPT OSS 20B, GPT OSS 120B
Alibaba Qwen3
23B A22B, 23B5B Thinking, Coder 480B
Meta Llama
Llama 4 Scout, 3.3 70B, 3.1 8B
MoonshotAI Kimi
Kimi K2
Microsoft Phi
Phi 4 Mini 3.8B
Hangzhou DeepSeek
DeepSeek V3.1, V3-0324, R1-0528
Z.ai
Z.AI GLM 4.5
Weave >
Traces
Explore and debug AI applications
Evaluations
Rigorous evaluations of AI applications
Playground
Explore prompts and models
Agents
Observability tools for agentic systems
Guardrails
Block prompt attacks and harmful outputs
Monitors
Continuously improve in production
Core
Registry
Publish and share your ML models and datasets
Artifacts
Version and manage your ML pipelines
SDK
Log ML experiments and artifacts at scale
Automations
Trigger workflows automatically
Solutions
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Train LLMs
Fine-tune LLMs
Computer Vision
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Industries
Autonomous Vehicles
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Canva
Learn how Canva leverages W&B to deploy models
Microsoft
Learn how Microsoft uses W&B for their ML projects
Toyota
Learn how Toyota uses W&B for autonomous driving
OpenAI
Learn how OpenAI Robotics uses W&B for large scale ML
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Announcing Serverless RL: Train agents without worrying about infra or GPUs
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