The main bottlenecks in putting a high-quality Machine Learning (ML) model into production are data quality issues and inefficient workflows. Most ML teams spend 80% of their time fixing and improving data sets to get better model performance, rather than focusing on experimentation or deployment. This leads to problems such as mispredictions, bias, and slow deployment cycles. To solve these issues, Galileo is developing an "ML data intelligence" platform that automates the analysis and iteration process for ML teams, providing critical insights on how to improve model accuracy and efficiency. The platform works by hooking into existing model training frameworks and providing features such as dataset ranking, annotation error detection, and drift information on unstructured data. By incorporating these signals into an efficient automation workflow, Galileo aims to help ML teams ship models faster and get high-quality models faster.