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description: Learn why thousands of companies rely on W&B as their system of record for training AI models and developing AI applications with confidence.
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Microsoft Phi Phi 4 Mini 3.8B: https://wandb.ai/site/inference-model/microsoft-phi-4-mini-3.8b/
Models Build and manage AI models: https://wandb.ai/site/models
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Skills Skills for coding agents: https://wandb.ai/site/skills/
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What is MLOps?: /site/articles/what-is-mlops/
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What is reinforcement learning?: /site/articles/reinforcement-learning-rl/
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[content]
Weights & Biases: The AI developer platform
Learn More
Learn more
For more information or if you need help
retrieving your data
, please contact Weights & Biases Customer Support at
support@wandb.com
Skip to content
Platform
Close Platform
Open Platform
Models
Experiments
Track and visualize your experiments
Sweeps
Optimize your hyperparameters
Tables
Visualize and explore your data
Reports
Document and share your AI insights
Serverless Training
Serverless RL
Fine-tune LLMs without managing GPUs
Serverless SFT
Teach LLMs new tasks
ART
Open-source RL framework
Ruler
Automated reward function for RL
Serverless Inference
OpenAI OSS
GPT OSS 20B, GPT OSS 120B
Alibaba Qwen3
235B A22B, 235B Thinking, Coder 480B
Meta Llama
Llama 4 Scout, Llama 3.3 70B, Llama 3.1 8B
MoonshotAI Kimi
Kimi K2.5
Microsoft Phi
Phi 4 Mini 3.8B
Hangzhou DeepSeek
DeepSeek V3.1, V3-0324, R1-0528
Z.ai
Z.AI GLM 5.0
Weave
Traces
Explore and debug AI applications
Evaluations
Rigorous evaluations of AI applications
Playground
Explore prompts and models
Monitors
Continuously improve in production
Core
Registry
Publish and share your AI models and datasets
Skills
Skills for coding agents
CoreWeave Sandboxes
Isolated environments to run agents
Automations
Trigger workflows automatically
ARIA
AI Research and Iteration Agent
Solutions
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Open Solutions
Use Cases
AI governance
Computer vision
Contact centers
Evaluations
Fine-tune LLMs
Physical AI
RAG
Train LLMs
Quant trading
AI governance
Computer vision
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Evaluations
Fine-tune LLMs
Physical AI
RAG
Train LLMs
Quant trading
Industries
Autonomous Vehicles
Communications
Financial Services
Healthcare & Life Sciences
Public Sector
Scientific Research
Autonomous Vehicles
Communications
Financial Services
Healthcare & Life Sciences
Public Sector
Scientific Research
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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
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Learn how Toyota uses W&B for autonomous driving
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CoreWeave Sandboxes: Isolated environments to run AI agents and model-generated code safely at scale.
AI is
easy
to
productionize
The AI developer platform to build AI agents, applications,
and models with confidence
W&B Weave: Build agentic AI applications
import weave weave.init("quickstart") @weave.op() def llm_app(prompt): pass # Track LLM calls, document retrieval, agent steps
get started with weave
W&B Models: Build AI models
import wandb run = wandb.init(project="my-model-training-project") run.config = {"epochs": 1337, "learning_rate": 3e-4} run.log({"metric": 42}) my_model_artifact = run.log_artifact("./my_model.pt", type="model")
get started with Models
REQUEST DEMO
The world’s leading AI teams trust Weights & Biases
Meet our customers
Weights & Biases AI developer platform
Models
Build and manage AI models
Training
Fine-tune AI models on agentic tasks
Inference
Serve hosted & fine-tuned AI models
Weave
Iterate, evaluate and monitor agents
Registry
Datasets | Models | Prompts | Code | Metadata
Core
Reports | Automations | SDK | Skills and MCP server for agents
Secure deployment
Saas | Dedicated | Customer-managed
Models
Build and manage AI models
Training
Fine-tune with serverless RL
Inference
Access and explore hosted AI models
Weave
Iterate, evaluate and monitor agents
Registry
Datasets | Models | Prompts | Code | Metadata
Core
Reports | Automations | SDK
Secure deployment
Saas | Dedicated | Customer-managed
Models
Build and manage AI models
Inference
Access and explore hosted AI models
Training
Fine-tune with serverless RL
Weave
Iterate, evaluate and monitor agents
Registry
Datasets
|
Models
|
Prompts
|
Code
|
Metadata
Core
Reports
|
Automations
|
SDK
Secure deployment
SaaS
|
Dedicated
|
Customer-managed
Now available:
Weights & Biases mobile app
The first iOS app to monitor AI experiments and track training runs anytime, anywhere.
Download now
Compliance-ready for the enterprise
Weights & Biases AI development platform is certified under ISO/IEC 27001:2022, ISO/IEC 27017:2015, and ISO/IEC 27018:2019, and is compliant with SOC 2 and HIPAA standards. Our platform also helps customers comply with NIST 800-53 and we are in alignment with GDPR requirements for processing personal information.
Learn more about compliance with different deployment options:
Multi-tenant
|
Dedicated
Certified under
ISO 27001:2022
ISO 27017:2015
ISO 27018:2019
Compliant with
SOC 2
HIPAA
NIST 800-53
GDPR
Get started with one line of code
“I love Weave for a number of reasons. The fact that I could just add a library to our code and all of a sudden I’ve got a whole bunch of information about the GenAI portion of our product, in Weights & Biases, which I was already using and very familiar with. All those things that I’m watching for the performance of our AI, I can now report on quickly and easily with Weave.”
Mike Maloney, Co-founder and CDO
Neuralift
INTEGRATE QUICKLY
LANGCHAIN
LLAMAINDEX
PyTorch
HF Transformers
Lightning
TensorFlow
Keras
Scikit-LEARN
XGBoost
import openai, weave weave.init("weave-intro") @weave.op def correct_grammar(user_input): client = openai.OpenAI() response = client.chat.completions.create( model="o1-mini", messages=[{ "role": "user", "content": "Correct the grammar:\n\n" + user_input, }], ) return response.choices[0].message.content.strip() result = correct_grammar("That was peace of cake!") print(result)
import weave from langchain_core.prompts import PromptTemplate from langchain_openai import ChatOpenAI # Initialize Weave with your project name weave.init("langchain_demo") llm = ChatOpenAI() prompt = PromptTemplate.from_template("1 + {number} = ") llm_chain = prompt | llm output = llm_chain.invoke({"number": 2}) print(output)
import weave from llama_index.core.chat_engine import SimpleChatEngine # Initialize Weave with your project name weave.init("llamaindex_demo") chat_engine = SimpleChatEngine.from_defaults() response = chat_engine.chat( "Say something profound and romantic about fourth of July" ) print(response)
import wandb # 1. Start a new run run = wandb.init(project="gpt5") # 2. Save model inputs and hyperparameters config = run.config config.dropout = 0.01 # 3. Log gradients and model parameters run.watch(model) for batch_idx, (data, target) in enumerate(train_loader): ... if batch_idx % args.log_interval == 0: # 4. Log metrics to visualize performance run.log({"loss": loss})
import wandb ‍ # 1. Define which wandb project to log to and name your run run = wandb.init(project="gpt-5", run_name="gpt-5-base-high-lr") ‍ # 2. Add wandb in your `TrainingArguments` args = TrainingArguments(..., report_to="wandb") ‍ # 3. W&B logging will begin automatically when your start training your Trainer trainer = Trainer(..., args=args) trainer.train()
from lightning.pytorch.loggers import WandbLogger # initialise the logger wandb_logger = WandbLogger(project="llama-4-fine-tune") # add configs such as batch size etc to the wandb config wandb_logger.experiment.config["batch_size"] = batch_size # pass wandb_logger to the Trainer trainer = Trainer(..., logger=wandb_logger) # train the model trainer.fit(...)
import wandb # 1. Start a new run run = wandb.init(project="gpt4") ‍ # 2. Save model inputs and hyperparameters config = wandb.config config.learning_rate = 0.01 ‍ # Model training here # 3. Log metrics to visualize performance over time ‍ with tf.Session() as sess: # ... wandb.tensorflow.log(tf.summary.merge_all())
import wandb from wandb.keras import ( WandbMetricsLogger, WandbModelCheckpoint, ) ‍ # 1. Start a new run run = wandb.init(project="gpt-4") ‍ # 2. Save model inputs and hyperparameters config = wandb.config config.learning_rate = 0.01 ... # Define a model # 3. Log layer dimensions and metrics wandb_callbacks = [ WandbMetricsLogger(log_freq=5), WandbModelCheckpoint("models"), ] model.fit( X_train, y_train, validation_data=(X_test, y_test), callbacks=wandb_callbacks, )
import wandb wandb.init(project="visualize-sklearn") ‍ # Model training here # Log classifier visualizations wandb.sklearn.plot_classifier(clf, X_train, X_test, y_train, y_test, y_pred, y_probas, labels, model_name="SVC", feature_names=None) ‍ # Log regression visualizations wandb.sklearn.plot_regressor(reg, X_train, X_test, y_train, y_test, model_name="Ridge") ‍ # Log clustering visualizations wandb.sklearn.plot_clusterer(kmeans, X_train, cluster_labels, labels=None, model_name="KMeans")
import wandb from wandb.xgboost import wandb_callback ‍ # 1. Start a new run run = wandb.init(project="visualize-models") ‍ # 2. Add the callback bst = xgboost.train(param, xg_train, num_round, watchlist, callbacks=[wandb_callback()]) ‍ # Get predictions pred = bst.predict(xg_test)
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