To help clinical teams with tasks such as detecting anomalies and drug discovery researchers with generating de novo molecules, algorithms need to be constantly updated, corrected, and retrained. However, tracking and logging machine learning experiments are often done manually, making it difficult to compare model candidates and causing friction in collaboration among machine learning engineering teams.
The Weights & Biases platform streamlines the productivity of machine learning developers with a standardized, automated system of record to capture and visualize the entire machine learning experimentation process.
With just a few lines of code, users can generate live charts immediately. Machine learning practitioners can instantly debug, compare, and reproduce models, architecture, hyperparameters, git commits, model weights, datasets, and predictions—all in a centralized dashboard that unlocks collaboration across teams of any size.