Weights & Biases

With a few lines of code, save everything you need to debug, compare and reproduce your models

Weights & Biases Overview

Experiment tracking, Datasetset tracking, Dataset visualization

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Categories:
All
MLOps
Data
Data+MLOps

System-wide Features:
Teamwork and Collaboration
Enterprise Security
Governance
Enterprise Support

MLOps Features:
Data Acquisition
Data Versioning
Data Visualization
Data Preparation
Data Pipelines
Data Labeling
AutoML
Featurization
Feature Store
ML Pipelines or Workflows
Model Registry
Model Marketplace
Model Training
Distributed Model Training
Model Debugging
Experiment Management
Deep Learning Support
Reinforcement Learning Support
Bias Detection and Mitigation
Model Explainability
Hyperparameter Optimization
Model Packaging
Model Deployment and Serving
Edge ML Support
Model Monitoring
Cost Management
ML Infrastructure Orchestration
Accelerator Support
Kubernetes Support

Experiment Management Sub-features:
Log: Data
Log: Code
Log: Parameters
Log: Metrics and Losses
Log: Descriptions
Log: Rich Media
Log: Hardware Consumption
Log: System Information
Log: Environment Configuration
Log: Files
Tracking: Organize Experiments
Tracking: Filter and Search
Tracking: Live Monitoring
Tracking: Can Track Thousands of Experiments
Visualize: Compare and Evaluate Experiments
Collaboration: Role Based Access Controls
Collaboration: Deep Links Into Assets
Collaboration: Customizable Experiment Reporting

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Additional Product Information

Product Links

Deploys On

Amazon Web Services
Google Cloud Platform
Microsoft Azure
Other Public Cloud
Kubernetes
NVIDIA
Private Cloud or Datacenter
SaaS

Pricing:

Free Version

Paid Version

Weights & Biases Features and Benefits

Benefits

Weights & Biases helps your ML team unlock their productivity by optimizing, visualizing, collaborating on, and standardizing their model and data pipelines – regardless of framework, environment, or workflow.

Think of W&B like GitHub for machine learning models. With a few lines of code, you can save everything you need to debug, compare and reproduce your models— architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions.

W&B’s lightweight integrations work with any Python script, and you can sign up for a free account and start tracking and visualizing models in 5 minutes.

Features

• W&B's dashboard lets you track your experiments, see live updates on model performance, check for overfitting, reproduce your best performances, and more.
• Artifacts gives you a bird's eye view of every step of model development so you can understand which data and which models depend on each other
• Tables lets you actually *see*, organize, and evaluate your data, unlocking insights about where your model is excelling---and where it isn't
• Working with a team on W&B means your colleagues can dig into and reproduce any of your experiments, collaborate on models, and seamlessly share insights with stakeholders

Weights & Biases Vendor Information

Vendor Overview

Think of W&B like GitHub for machine learning models. With a few lines of code, save everything you need to debug, compare and reproduce your models - architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions.

W&B’s lightweight integrations work with any Python script, and you can sign up for a free account and start tracking and visualizing models in 5 minutes.

Used by top researchers including teams at Qualcomm, NVIDIA, OpenAI, Lyft, Pfizer, Toyota, Github, and MILA, W&B is part of the new standard of best practices for machine learning.