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Code Generation—Predictions with Scoring Code

Community Team
Community Team
3 0 1,356

(Part of an MLOps learning session series.)

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


  • 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 Community:

DataRobot licensed customers: search in-app Platform Documentation for Scoring Code and Batch Prediction API.

Let us know what you think

Have questions not answered during the learning session? Want to continue your conversation with Brent and Rajiv? Post Your Comment here or send email to We look forward to hearing from you!

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DataRobot Release 7.1
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The acquisition of Zepl and integration of its self-service data science notebook solution provides additional flexibility for data scientists who prefer to code. Jason's blog post provides an end-to-end DataRobot demo that uses Zepl notebooks. You can check out Zepl today.

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