Project Robbie, developed in collaboration with Boston University, eliminates infrastructure complexity, enabling analysts to train and deploy AI models effortlessly. Early adopters at UIUC and the University of Rochester have already leveraged Robbie to streamline workflows.
Preparing, transforming, and integrating data into the model training cycle currently requires specialized skills, large amounts of code, and often breaks when the dataset changes. Finding outliers, extracting features, and normalizing the dataset require data science skills well beyond most analysts. We are automating this process with a set of AI models.
Our unique ML model that automatically selects the best cloud infrastructure for conducting your experiment. Designed to recognize your data sets and simulations and matches it to the optimal cloud infra with optimized instance selection and LLM analysis of compile tree. Built-in cost predictions to allocate funding resources.