Weights & Biases

Replay past experiments with different parameters to understand model behavior.

Compare your models against industry benchmarks and baseline models.

Compare experiments across multiple projects in a single view.

Custom evaluation metrics and scoring functions for model comparison.

AI-powered analysis to identify which hyperparameters have the most impact on performance.

Powerful query language for filtering and analyzing experiment runs programmatically.

Create shareable reports documenting experiments, findings, and model comparisons.

Real-time collaboration features for sharing experiments and insights across teams.

Log and compare metrics, hyperparameters, and outputs from training runs automatically.

Centralized versioning and management of trained models with metadata tracking.

Complete history and lineage of all experiments with ability to restore and replay runs.

Track the relationship between datasets, models, and experiments for full reproducibility.

Store and manage files, models, and datasets as versioned artifacts with automatic cleanup.

Track the complete lineage of data through preprocessing and training pipelines.

Version control for datasets with lineage tracking and reproducibility.

Comprehensive REST API for programmatic access to experiments and data.

Export experiment data to CSV, JSON, or connect to external analytics tools.

Run Weights & Biases locally without internet connectivity for offline development.

Send notifications to external services when experiments complete or alerts trigger.

Lightweight Python SDK for logging and tracking experiments with minimal code changes.

Create public or private leaderboards to track and compare model performance.

Interactive IDE for building, testing, and versioning LLM prompts.

Track and version LLM prompts along with their inputs, outputs, and performance metrics.

AI-powered tracing and evaluation framework for tracking LLM applications end-to-end.

Log custom Python objects and data types alongside standard metrics.

Log images, videos, audio, and 3D objects to visualize model outputs and predictions.

Log structured data as tables for detailed analysis of predictions and errors.

Track model staging, approval workflows, and deployment history for compliance.

Set up custom alerts for anomalies, training failures, or metric thresholds.

Automatically log GPU, CPU, memory, and disk usage during training.

Organize projects and experiments into workspaces with role-based access control.

Automatically generate and email reports on a schedule for stakeholders.

Automatically capture and save code snapshots with each experiment run.

Automatically capture Python package versions and dependencies for reproducibility.

Fine-grained role-based access control for team collaboration and security.

Automatically detect performance regressions in new model versions.

Create custom charts and visualizations to analyze training data and model performance.

Visualize relationships between hyperparameters and metrics with parallel coordinates.

Interactive dashboards for monitoring training progress and model metrics in real-time.

Create interactive scatter plots to analyze relationships between metrics.