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What is the purpose of a Dataiku "dashboard", and how does it typically relate to the underlying analysis/models built in a project?
Questions in this quiz
What is the purpose of a Dataiku "dashboard", and how does it typically relate to the underlying analysis/models built in a project?
How does Dataiku support integrating custom Python or R code within an otherwise largely visual project Flow?
What is a Dataiku "recipe"?
What is the purpose of Dataiku supporting multiple, DIFFERENT compute engines/backends (e.g. running a recipe's logic on the local Dataiku server, pushing it down to run as SQL directly in a connected database, or executing it on a Spark cluster) for the same visual recipe?
How does Dataiku typically support deploying a trained machine learning model for actual production use (beyond just training/evaluating it within the platform)?
What is the purpose of "scenarios" in Dataiku, and what problem do they solve for operationalizing a data project?
What is the purpose of Dataiku's visual, no-code data preparation interface, and who is it primarily aimed at making data work accessible to?
What is the purpose of automated "checks" that can be configured on datasets or within scenarios in Dataiku?
What is the significance of Dataiku providing "plugins" and connectors for various external data sources and services?
What is Dataiku (Dataiku DSS)?
What is a Dataiku "dataset" (as a concept within the platform), and what kinds of underlying data sources can it represent?
What is the purpose of Dataiku's "Visual Machine Learning" feature, as distinct from writing custom ML code directly?