Data Sciencing with AtScale: Introducing Data Science Use Cases and AI-Link

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John Lynch

Sales Engineer at AtScale

Chris Oshiro Headshot, Field Cto At AtScale
Chris Oshiro

Field CTO at AtScale

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Introducing Data Science Use Cases and AI-Link

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Now on-demand:

There’s been a widening gap between BI and Data Science analytics developing. Most of this is due to two forces. First – the well established and mission critical analytics that the BI world has dominated and continues to enhance. Second – the rocket ship revolution coming from data science analytics offering us insights never before available. What’s happening? Massive innovations in both camps, but not nearly enough work has been done to bring these camps together. An AtScale semantic layer can serve as a “feature hub,” where data scientists, business SMEs, and data teams can collaborate on designing new features and maintaining the data pipelines that support production models. AtScale AI-Link provides python connectivity between the semantic layer, notebooks, and automl platforms. Beyond feature management, AtScale can help data science teams publish AI-generated insights (e.g. predictions) to a broader audience but making results accessible in existing dashboards and reporting platforms.

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We discuss:

  • Pull business vetted calculations via python
  • Leverage metadata from the semantic layer for feature engineering
  • Write features and predictions back to the semantic layer for consumption across tools