Predictive modeling is transforming the nature of how businesses are run. Models provide insights
that let leaders reliably manage for the future instead of using indicators that only show what
happened in the past. A model-driven enterprise relies on data science – particularly, upon data
scientists who possess the technical skills to execute on the promise of predictive modeling. For
most organizations, early forays into modeling often yield quick results on small trial projects, but
efforts to scale data science for enterprise production usually fall flat.
The culprit is the lack of scalable and flexible tooling and workflows that allow large teams of data
scientists to systematically experiment and collaborate on projects that are unlike typical software
or product development. Without the freedom and ability to try new tools, algorithms, and
infrastructure (e.g., GPUs and distributed compute, productivity of data scientists is often spotty,
with massive efforts yielding minimal results. The answer is a specialized data science workbench
– a self-service software platform that instantly spins up any tool, package or compute resource
needed by teams to do their work quickly and effectively, without requiring IT administration for
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