Software engineering and design have developed patterns and practices over the last 50 years that allow them to quickly validate and implement ideas. In contrast, data science is often characterized by slow feedback loops with long periods of analysis and discovery followed by implementation. This workflow makes it more difficult to iterate and leads to problems that software engineering best practices were developed to address. However, it can be unclear how to apply these practices to data science. This talk will explain how Very adapts practices from software engineering and design to our data science projects to develop and deploy models with agility.