Weights & Biases

ML experiment tracking and model management platform

freemiumproductionmlopsexperiment-trackingmonitoringcollaboration

Memory Types

Integrations

pytorch, tensorflow, keras, huggingface, langchain


Overview


Weights & Biases (W&B) is the leading platform for ML experiment tracking, model management, and collaboration. Used by leading AI companies including OpenAI, the platform provides tools for tracking experiments, visualizing results, comparing models, and managing the ML lifecycle. W&B has become essential infrastructure for ML teams.


The platform emphasizes developer experience with easy integration, beautiful visualizations, and collaborative features. W&B helps teams answer questions like "which experiment performed best?" and "what changed between model versions?" - critical for iterative ML development.


Key Features


  • **Experiment Tracking**: Log metrics, parameters, artifacts
  • **Model Registry**: Manage model versions and deployments
  • **Visualizations**: Interactive charts and dashboards
  • **Hyperparameter Tuning**: Optimize model configurations
  • **Collaboration**: Share experiments with team
  • **Artifacts**: Version datasets and models
  • **Reports**: Share results with stakeholders
  • **Integrations**: Works with major ML frameworks

  • When to Use Weights & Biases


    Weights & Biases is ideal for:

  • ML teams running many experiments
  • Organizations training custom models
  • Teams needing reproducible ML workflows
  • Collaborative ML development
  • Production model monitoring
  • Academic ML research

  • Pros


  • Industry-standard experiment tracking
  • Beautiful visualizations
  • Easy to integrate
  • Free tier for individuals
  • Great developer experience
  • Strong community
  • Good documentation
  • Active development

  • Cons


  • Can be expensive for large teams
  • Requires cloud connection
  • Learning curve for advanced features
  • Free tier has limitations
  • Some features only in paid tiers
  • Can generate lots of data
  • Primarily for training (less for inference)
  • May be overkill for simple projects

  • Pricing


  • **Free**: Individual use, public projects
  • **Team**: $50/user/month
  • **Enterprise**: Custom pricing
  • **Academic**: Free for researchers