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What is a Dataiku "Flow", and what does it represent?
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What is a Dataiku "Flow", and what does it represent?
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 a reasonable concern a team might have when adopting a comprehensive, all-in-one platform like Dataiku, compared to assembling a data science workflow from several separate, best-of-breed specialized tools?
What is the purpose of "scenarios" in Dataiku, and what problem do they solve for operationalizing a data project?
What is Dataiku (Dataiku DSS)?
What is a reasonable way Dataiku's "Flow" visualization specifically helps with understanding and maintaining a complex, multi-step data pipeline over time, compared to a pipeline defined purely as a sequence of disconnected scripts with no visual representation?
What is the purpose of automated "checks" that can be configured on datasets or within scenarios in Dataiku?
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 Dataiku's "Visual Machine Learning" feature, as distinct from writing custom ML code directly?
How does Dataiku support integrating custom Python or R code within an otherwise largely visual project Flow?
What is a reasonable justification for a business analyst (without strong coding skills) being able to meaningfully contribute to a Dataiku project alongside a professional data scientist, compared to a purely code-first tool that would require everyone involved to write code?
What is a reasonable strategy for deciding when to use Dataiku's visual "Prepare" recipe versus writing an equivalent Python/pandas script for a given data transformation step?