Weights & Biases helps machine learning teams build better models. Practitioners can debug, compare and reproduce their models — architecture, hyperparameters, git commits, model weights, GPU usage, datasets and predictions — and collaborate with their teammates. The platform enables companies to:
--Track, compare, and visualize with 5 lines of code and add a few lines to scripts to start logging results. The platform integration works with any Python script.
--The ability to keep track of what was tried, and plan next steps; share graphs, notes and dynamic experiments with flexible formats and easily invite collaborators to edit and comment on work.
--Build a dependency graph with the ability to trace the flow of data through your pipeline, so you know exactly which datasets feed into the models. the ability to visualize and query interesting rows from datasets. Group, sort, filter, generate calculated columns, and create charts from tabular data.
--Visualize which hyperparameters affect the metrics you care about. W&B comes with default visualizations that make it easy to get started without writing custom code to compare experiments.