Creating a Faster Cooperative Data Science Workflow

This white paper discusses the integration of Datameer and Amazon SageMaker to create an efficient cooperative data science workflow. It outlines the importance of well-prepared data sets for machine learning and describes how the data science lifecycle involves multiple stages that require deep analysis and exploration. The paper emphasizes the need for data preparation, exploration, and machine learning to be conducted in tandem. It details how Datameer can enhance the machine learning process in SageMaker by facilitating data profiling, visual exploration, and algorithmic exploration. The document also presents a specific example involving telematics data to illustrate the workflow, showing how data can be prepared and features engineered before applying a random forest model in SageMaker. The iterative nature of this process allows for continuous improvement and validation of machine learning models, highlighting the symbiotic relationship between data preparation and machine learning platforms.