Traditional machine-learning projects can take months: cleaning data, choosing models, tuning parameters, validating, deploying. We asked a simple question — what if most of that could be automated?
Our research focuses on automated pipelines that handle the repetitive, time-consuming steps of ML development while keeping model quality high.
By compressing the lifecycle from months to hours, we make ML practical for teams that never had the time or specialists to use it.
