We know that adopting documentation, testing, and version control mechanisms are important for creating a culture of reproducibility in data science. But once you’ve embraced some basic development best practices, what comes next? What does it take to feel confident that our data products will make it to production? This talk will cover case studies in how I work with R users at various organizations to bridge the gaps that form between development and production. I’ll cover reasons why CI/CD tools can enhance reproducibility for R and data science, showcase practical examples like automated testing and push-based application deployment, and point to simple resources for getting started with these tools in a number of different environments.