Case Study: RadAI ships production ML models faster with Weights & Biases

RadAI cuts model deployment overhead with Weights & Biases

RadAI is a company that develops AI tools for radiologists to reduce burnout and improve efficiency. Their challenge was the need for better synchronization between machine learning researchers and software engineers to accelerate moving models from research into production.

By implementing the Weights & Biases platform, specifically the W&B model registry, RadAI created a pipeline that automates deployments. When a researcher tags an artifact in the registry, it triggers an end-to-end CI/CD pipeline. This solution from Weights & Biases reduced the need for meetings and cross-team coordination, allowing the team to ship reliable AI models to production faster and with less overhead.

RadAI