Machine learning without experiment tracking is like software engineering without version control. You are just guessing and hoping you remember what worked.
I built CrowdFlower, sold it, and then spent two years doing ML research. The tooling was so bad I kept building internal tools just to get work done. That became Weights & Biases.
We have a bottom-up adoption model. One researcher on a team starts using W&B, shows their teammates the dashboards, and suddenly the whole lab is on it. We do not need a sales team for that.
OpenAI, DeepMind, Toyota, Samsung — they all use us. When the best AI teams in the world choose your tool, you know you are building the right thing.
The AI gold rush needs picks and shovels. Everyone wants to build the model. We build the infrastructure that makes the model actually work in production.
Most ML projects fail not because the model is bad but because nobody can reproduce the results, nobody tracked the hyperparameters, and nobody knows which version of the data was used. We fix all three.