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Models: MLOps solution
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   Track and visualize your ML experiments
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   Optimize your hyperparameters
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   Register and manage your ML models
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   Trigger workflows automatically
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   Package and run your ML workflow jobs

Weave: LLMOps solution
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   Explore and debug LLM applications
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   Rigorous evaluations of GenAI applications

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   Version and manage your ML pipelines
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 * Computer Vision
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 * Develop with LLMs
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 * Computer Vision
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Models: MLOps solution
 * Experiments
   Track and visualize your ML experiments
 * Sweeps
   Optimize your hyperparameters
 * Model Registry
   Register and manage your ML models
 * Automations
   Trigger workflows automatically
 * Launch
   Package and run your ML workflow jobs

Weave: LLMOps solution
 * Traces
   Explore and debug LLM applications
 * Evaluations
   Rigorous evaluations of GenAI applications

Core platform
 * Artifacts
   Version and manage your ML pipelines
 * Tables
   Visualize and explore your ML data
 * Reports
   Document and share your ML insights

USE CASES

 * Develop with LLMs
 * Train LLMs
 * Fine-tune LLMs
 * Computer Vision
 * Time Series
 * Recommender Systems
 * Classification & Regression

 * Develop with LLMs
 * Train LLMs
 * Fine-tune LLMs
 * Computer Vision
 * Time Series
 * Recommender Systems
 * Classification & Regression

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 * Financial Services
 * Scientific Research
 * Communications
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 * Healthcare and Life Sciences
 * Academic Research

 * Autonomous Vehicles
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 * Public Sector
 * Healthcare and Life Sciences
 * Academic Research

 * MLOps For Enterprise
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 * Deployment Options
 * Build vs. Buy
 * MLOps Maturity Assessment
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Platform
Models: MLOps solution
 * Experiments
   Track and visualize your ML experiments
 * Sweeps
   Optimize your hyperparameters
 * Model Registry
   Register and manage your ML models
 * Automations
   Trigger workflows automatically
 * Launch
   Package and run your ML workflow jobs

Weave: LLMOps solution
 * Traces
   Explore and debug LLM applications
 * Evaluations
   Rigorous evaluations of GenAI applications

Core platform
 * Artifacts
   Version and manage your ML pipelines
 * Tables
   Visualize and explore your ML data
 * Reports
   Document and share your ML insights

Solutions

USE CASES

 * Develop with LLMs
 * Train LLMs
 * Fine-tune LLMs
 * Computer Vision
 * Time Series
 * Recommender Systems
 * Classification & Regression

 * Develop with LLMs
 * Train LLMs
 * Fine-tune LLMs
 * Computer Vision
 * Time Series
 * Recommender Systems
 * Classification & Regression

INDUSTRIES

 * Autonomous Vehicles
 * Financial Services
 * Scientific Research
 * Communications
 * Public Sector
 * Healthcare and Life Sciences
 * Academic Research

 * Autonomous Vehicles
 * Financial Services
 * Scientific Research
 * Communications
 * Public Sector
 * Healthcare and Life Sciences
 * Academic Research

Enterprise
 * MLOps For Enterprise
 * W&B For Teams
 * Deployment Options
 * Build vs. Buy
 * MLOps Maturity Assessment
 * Security

 * MLOps For Enterprise
 * W&B For Teams
 * Deployment Options
 * Build vs. Buy
 * MLOps Maturity Assessment
 * Security

Resources
 * Resource Library
 * Case Studies
 * Whitepapers
 * Articles
 * Partners

 * Resource Library
 * Case Studies
 * Whitepapers
 * Articles
 * Partners

 * Blog & Tutorials
 * Events
 * ML Courses
 * Gradient Dissent Podcast
 * Our Community

 * Blog & Tutorials
 * Events
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 * Gradient Dissent Podcast
 * Our Community

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THE AI DEVELOPER PLATFORM

Train and fine-tune models, manage models from experimentation to production,
and track and evaluate LLM applications.

GET STARTED
REQUEST DEMO



THE WORLD’S LEADING AI TEAMS TRUST WEIGHTS & BIASES



Meet our customers


A SYSTEM OF RECORD DEVELOPERS WANT TO USE


EXPERIMENTS

Track and visualize your ML experiments


SWEEPS

Optimize your
hyperparameters


LAUNCH

Package and run your
ML workflow jobs


MODEL REGISTRY

Register and manage
your ML models


AUTOMATIONS

Trigger workflows
automatically


MODELS

Build & Fine-
tune models




TRACES

Monitor and debug
LLMs and prompts


WEAVE

Develop GenAI
applications


EVALUATIONS

Rigorous evaluations
of GenAI applications




W&B CORE: A FOUNDATIONAL FRAMEWORK SUPPORTING AI DEVELOPERS


ARTIFACTS

Version and manage your ML pipelines


TABLES

Visualize and explore your ML data


REPORTS

Document and share your ML insights


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

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


Phil Brown, Director of Applications
Graphcore
INTEGRATE QUICKLY LANGCHAIN LLAMAINDEX PyTorch HF Transformers Lightning
TensorFlow Keras Scikit-LEARN XGBoost

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)


THE LEADING AI DEVELOPER PLATFORM THAT PROVIDES VALUE TO YOUR ENTIRE TEAM

    I train models     I lead ML projects     I manage model production     I
develop with LLMs
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


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

See build vs buy comparison

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.


FOR GENERATIVE AI SOFTWARE DEVELOPERS


DESIGNED TO HELP SOFTWARE DEVELOPERS DEPLOY GENAI APPLICATIONS WITH CONFIDENCE

The tools developers need to evaluate, understand and iterate on dynamic,
non-deterministic large language models.


FOR GENERATIVE AI SOFTWARE DEVELOPERS


AUTOMATICALLY LOG ALL INPUTS, OUTPUTS AND TRACES FOR SIMPLE DEBUGGING

Weave captures all input and output data and builds a tree to give developers
full observability and understanding about how data flows through their
applications. 

FOR GENERATIVE AI SOFTWARE DEVELOPERS


RIGOROUS EVALUATION FRAMEWORKS TO DELIVER ROBUST LLM PERFORMANCE

Compare different evaluations of model results against different dimensions of
performance to ensure applications are as robust as possible when deploying to
production.


View other personas


BUILD AND FINE-TUNE MODELS, AND DEVELOP GENAI APPLICATIONS WITH CONFIDENCE

SIGN UP
REQUEST DEMO
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THE PLATFORM

 * Experiments
 * Sweeps
 * Model registry
 * Automations
 * Launch
 * Weave
 * Traces
 * Evaluations
 * Artifacts
 * Tables
 * Reports

 * Experiments
 * Sweeps
 * Model registry
 * Automations
 * Launch
 * Weave
 * Traces
 * Evaluations
 * Artifacts
 * Tables
 * Reports

ARTICLE

 * What is MLOps?
 * Experiment tracking
 * Hyperparameter tuning
 * ML model registry
 * What is CI/CD?
 * Data and model versioning
 * What is LLMOps?
 * LLM evaluations

 * What is MLOps?
 * Experiment tracking
 * Hyperparameter tuning
 * ML model registry
 * What is CI/CD?
 * Data and model versioning
 * What is LLMOps?
 * LLM evaluations

RESOURCES

 * Documentation
 * Community forum
 * Articles
 * Blog and tutorials
 * Podcast

 * Documentation
 * Community forum
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 * Blog and tutorials
 * Podcast

COMPANY

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