Code Generation. Predictions with Scoring Code in DataRobot

May 29, 2020
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· 1 min read

This post was originally part of the DataRobot Community. Visit now to browse discussions and ask questions about DataRobot, AI Cloud, data science, and more.

Machine learning use cases are all unique, and model deployment doesn’t have a “one-size-fits-all” solution. DataRobot provides some incredibly flexible and scalable APIs for deploying models, but what can you do if that approach doesn’t fit your needs?

In this session we’ll explore DataRobot’s exportable scoring code and several ways you can integrate these models into your data pipelines to achieve real operational value.

Some topics we’ll cover:

  • When it makes sense to use scoring code vs the prediction APIs
  • The basic internal structure of the scoring code packages
  • Basic scoring functionality
  • Scoring large datasets via Spark
  • Custom integration, including MLOps

Hosts

  • Brent Hinks (DataRobot, AI Engineer)
  • Rajiv Shah (DataRobot, Customer Facing Data Scientist)
  • Jack Jablonski (DataRobot, AI Success Manager)

Now what?

After watching the learning session, you should check out these resources for more information.

DataRobot Public Platform Documentation:

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About the author
Linda Haviland
Linda Haviland

Community Manager

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