The developer-first MLOps platform

Weights & Biases makes it easy to track your experiments, manage & version your data, and collaborate with your team so you can focus on building the best models.

Loved by

500,000+

ML practitioners

The world’s leading ML teams trust us

Meet our customers

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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
workflow

Models

Model lifecycle
management

Integrate quickly,
track & version automatically

Track, version and visualize with just 5 lines of code
Reproduce any model checkpoints
Monitor CPU and GPU usage in real time

Try a live notebook

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“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
‍
# Model training here
# 3. Log metrics to visualize performance over time
for i in range(10):
  run.log({"loss": loss})

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

# 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
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
‍
# 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()

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

Visualize live metrics, datasets, logs, code, and system stats in a centralized location
Analyze collaboratively across your team to uncover key insights
Compare side-by-side to debug easily, and build iteratively

Watch demo

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“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

Experiment collaboratively to find the best model
Evaluate models, discuss bugs, and demonstrate progress
Inform stakeholders with configurable reports

View example reports

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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

ML framework

Pytorch

XGBoost

HuggingFace

TensorFlow

GPT-3

spaCy

& 9,000+ ML libraries and repos

Training environment

Sagemaker

Azure ML

Run.ai

Vertex AI

NVIDIA DGX

Anyscale

W&B SaaS cloud

W&B dedicated cloud

Customer-managed

Deploy anywhere, any way

Workflow orchestration

Airflow

Github Actions

Metaflow

Kubeflow

Jenkins

Flyte

Inference environment

Sagemaker

Azure ML

Run.ai

Vertex AI

NVIDIA DGX

OctoML

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.

See all deployment options →

See W&B partners & integrations →

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.

Read our W&B MLOps Whitepaper →

FOR ML LEADERS

Deliver ROI in the real world

Accelerate innovation to market and deliver ongoing business impact. W&B enables running 1000s of experiments iteratively and collaboratively, all while continuously optimizing every part of your ML system over time.

See build vs buy comparison

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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.