May 2022 Summaries

Grid is a cloud-based machine learning platform designed to reduce infrastructure-management burdens and help data scientists, researchers, and engineers move more quickly from model prototyping to training and production. It addresses common adoption challenges such as limited local computing capacity, cloud-cost concerns, debugging, collaboration, and the high rate of models that fail to reach deployment. The platform supports CPU and GPU infrastructure without requiring code changes, along with shared datastores, transient Runs for Python, Julia, and R workloads, interactive Jupyter-based Sessions, and artifact storage and downloads. Grid emphasizes scalable training, parallel hyperparameter search, reproducibility, and a unified interface intended to let users focus on model development rather than operational tasks. Its stated mission is supported by community feedback, research and development, and ongoing product innovation.