The AI developer platform

Build better models faster.

Quickly track experiments, version and iterate on datasets, evaluate model performance, reproduce models, and manage your ML workflows end-to-end.

The world’s leading ML teams trust W&B

The Weights & Biases platform helps you streamline your ML workflow from end to end

Experiments

Experiment tracking

Reports

Collaborative dashboards

Artifacts

Dataset and
model versioning

Tables

Interactive data visualization

Sweeps

Hyperparameter optimization

Launch

Automate ML workflows

Models

Model lifecycle management

LLM Monitoring

Observability for production ML

Prompts

LLMOps and prompt engineering

Weave

Interactive
ML app builder

Integrate quickly, track & version automatically

“We’re now driving 50 or 100 times more ML experiments versus what we were doing before.”

Phil Brown, Director of Applications
Graphcore
				
					import wandb

# 1. Start a W&B run
run = wandb.init(project="my_first_project")
# 2. Save model inputs and hyperparameters
config = wandb.config
config.learning_rate = 0.01
# 3. Log metrics to visualize performance over time
for i in range(10):
 run.log({"loss": loss})
				
			
				
					import wandb
import os

# 1. Set environment variables for the W&B project and tracing.
os.environ["LANGCHAIN_WANDB_TRACING"] = "true" os.environ["WANDB_PROJECT"] = "langchain-tracing"

# 2. Load llms, tools, and agents/chains

llm = OpenAI(temperature=0)
tools = load_tools(["llm-math"], llm=llm)
agent = initialize_agent(
     tools, llm,      agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,      verbose=True
)

# 3. Serve the chain/agent with all underlying complex llm interactions automatically traced and tracked

agent.run("What is 2 raised to .123243 power?")
				
			
				
					import wandb
from llama_index import ServiceContext
from llama_index.callbacks import CallbackManager,      WandbCallbackHandler

# initialise WandbCallbackHandler and pass any wandb.init args

wandb_args = {"project":"llamaindex"}
wandb_callback =      WandbCallbackHandler(run_args=wandb_args)

# pass wandb_callback to the service context

callback_manager = CallbackManager([wandb_callback])
service_context =      ServiceContext.from_defaults(callback_manager=
     callback_manager)
				
			
				
					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)
				
			

Visualize your data and uncover critical insights

“Saving everything in your model pipelines is essential for serious machine learning: debugging, provenance, reproducibility. W&B is a great tool for getting this done.”

Richard Socher, fmr Chief Data Scientist
Salesforce

Improve performance so you can evaluate and deploy with confidence

W&B allows us to scale up insights from a single researcher to the entire team and from a single machine to thousands.

Wojciech Zaremba, Co-Founder
OpenAI

The Weights & Biases ecosystem

Manage your entire ML lifecycle with a unified interface over any ML infrastructure

Integrations with 19,000+ ML Libraries & Repos
Training environment
Workflow orchestration
Inference environment

The leading ML platform that provides value to your entire team

FOR ML PRACTITIONERS

The user experience that makes redundant work disappear

Track every detail of your ML pipeline automatically. Visualize results with relevant context. Drag & drop analysis to uncover insights – your next best model is just a few clicks away

FOR ML PRACTITIONERS

The ML workflow co-designed with ML engineers

Build streamlined ML workflows incrementally. Configure and customize every step. Leverage intelligent defaults so you don’t have to reinvent the wheel.

FOR ML PRACTITIONERS

A system of record that makes all histories reproducible and discoverable

Reproduce any experiment instantly. Track model evolution with changes explained along the way. Easily discover and build on top of your team’s work.

FOR MLOps

Flexible deployments, easy integration

Deploy W&B to your infrastructure of choice, W&B-managed or Self-managed available. Easily integrate with your ML stack & tools with no vendor lock-in.

FOR MLOps

Bridge ML Practitioners and MLOps

Automate and scale ML workloads in one collaborative interface – ML practitioners get the simplicity, MLOps get the visibility.

FOR MLOps

Scale ML production with governance

A centralized system of record for all your ML projects. Manage model lifecycle and CI/CD to accelerate production. Understand model evolution and explain business impact to leadership.
FOR ML LEADERS

The user experience that makes redundant work disappear

Track every detail of your ML pipeline automatically. Visualize results with relevant context. Drag & drop analysis to uncover insights – your next best model is just a few clicks away

FOR ML LEADERS

Any industry, any use case

Customers from diverse industries trust W&B with a variety of ML use cases. From autonomous vehicle to drug discovery and from customer support automation to generative AI, W&B’s flexible workflow handles all your custom needs.

FOR ML LEADERS

Let the team focus on value-added activities

Only focuses on core ML activities – W&B automatically take care of boring tasks for you: reproducibility, auditability, infrastructure management, and security & governance.

Future-proof your ML workflow – W&B co-designs with OpenAI and other innovators to encode their secret sauce so you don’t need to reinvent the wheel.

Never lose track of another ML project

Trusted by 800,000+ machine learning practitioners at 900+ companies and research institutions

"W&B was fundamental for launching our internal machine learning systems, as it enables collaboration across various teams."

Hamel Husain
GitHub

"W&B is a key piece of our fast-paced, cutting-edge, large-scale research workflow: great flexibility, performance, and user experience."

Adrien Gaidon
Toyota Research Institute

"W&B allows us to scale up insights from a single researcher to the entire team and from a single machine to thousands."

Wojciech Zaremba
Co-Founder of OpenAI

Featured Content

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