A training loop for balancing AI model quality, cost and speed

A user describes their Labs team's iterative workflow: pick a problem or agent to improve, curate data and evaluations or test environments, then run training experiments while measuring every tweak. They credit Baseten with supporting those experiments and argue that the infrastructure and tools now let teams run hundreds of experiments to find the best quality, cost and speed trade-off for their customers and use cases.