Rafi is a Sr. Solutions Architect at Databricks where he specializes in enabling customers to scale their R workloads with Spark. He is also the primary author of the R User Guide to Databricks and the bricksteR package. In his spare time he enjoys gardening with native plants, cooking up a storm, and long video game sessions with his three children.
Historically it has been challenging for R developers to build and share data products that use Apache Spark. In this talk, learn how you can publish Shiny apps that leverage the scale and speed of Databricks, Spark and Delta Lake, so your stakeholders can better leverage insights from your data in their decision making. They will walk through how to decouple a Shiny app from a Spark cluster without losing the ability to query billions of rows with Delta Lake. Learn how to safely promote models from development to production with the MLflow Model Registry on Databricks. By tracking model experimentation with MLflow and managing the lifecycle with the Registry, organizations can improve reproducibility and governance when publishing artifacts to RStudio Connect for batch or online scoring with Shiny or Plumber APIs.
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