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Weights & Biases: The AI Developer PlatformWeights & Biases is the leading AI developer platform to train and fine-tune models, manage models from experimentation to production, and track and evaluate GenAI applications powered by LLMs.Weights & Biases: The AI Developer Platform Skip to content Platform Close Platform Open Platform Models: MLOps solution ExperimentsTrack and visualize your ML experiments SweepsOptimize your hyperparameters RegistryPublish and share your ML models and datasets AutomationsTrigger workflows automatically LaunchPackage and run your ML workflow jobs Weave: LLMOps solution TracesExplore and debug LLM applications EvaluationsRigorous evaluations of GenAI applications Core platform ArtifactsVersion and manage your ML pipelines TablesVisualize and explore your ML data ReportsDocument and share your ML insights SDKLog ML experiments and artifacts at scale Solutions Close Solutions Open 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 Communications Financial Services Healthcare and Life Sciences Public Sector Scientific Research Autonomous Vehicles Communications Financial Services Healthcare and Life Sciences Public Sector Scientific Research Enterprise Close Enterprise Open 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 Close Resources Open Resources Resource Library Case Studies Whitepapers Articles Partners Resource Library Case Studies Whitepapers Articles Partners Blog & Tutorials Events AI Courses Gradient Dissent Podcast Our Community Blog & Tutorials Events AI Courses Gradient Dissent Podcast Our Community Company Close Company Open Company Careers Trust & security Legal About Us Careers Trust & security Legal About Us Docs Pricing LOG IN Sign up Stay on the AI frontier Weights & Biases is the AI developer platform powering the GenAI industry. Train your own foundation models, fine-tune someone else’s foundation model, manage models from experimentation to production, or develop AI applications powered by frontier LLMs. W&B Models: Train and productionize models import wandb run = wandb.init(project="my-model-training-project") run.config = {"epochs": 1337, "learning_rate": 3e-4} run.log({"metric": 42}) model_art = run.log_artifact("./model.pt", type="model") get started with Models W&B Weave: Develop GenAI applications import weave weave.init("quickstart") @weave.op() def llm_app(prompt): pass # Track LLM calls, doc retrieval, agent steps get started with weave REQUEST DEMO The world’s leading AI teams trust Weights & Biases Meet our customers A system of record developers want to use ExperimentsTrack and visualize your ML experiments SweepsOptimize your hyperparameters LaunchPackage and run your ML workflow jobs RegistryPublish and share your ML models and datasets AutomationsTrigger workflows automatically ModelsTrain, fine-tune, and productionize models TracesMonitor and debug LLMs and prompts WeaveDevelop GenAI applications EvaluationsRigorous evaluations of GenAI applications W&B Core: A foundational framework supporting AI developers ArtifactsVersion and manage your ML pipelines TablesVisualize and explore your ML data ReportsDocument 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": 2**-i}) 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) 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 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 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 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 X-twitter Linkedin-in Youtube The Platform Experiments Sweeps Registry Automations Launch Weave​ Traces Evaluations Artifacts Tables Reports Experiments Sweeps Registry Automations Launch Weave​ Traces Evaluations Artifacts Tables Reports Article What is MLOps? Experiment tracking Hyperparameter tuning ML model registry What is CI/CD? 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