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Dynamically deploy, update, and replace decisioning models with Machine Learning Operations (MLOps) (8.6)

Updated on May 12, 2021

Pega’s Machine Learning Operations (MLOps) automates the end-to-end process for model import and deployment with the use of Pega’s own APIs, so you can replace an underperforming model with a new one from any source – PMML, H2O MOJO, Pega OXL model files, Google AI Platform or Amazon SageMaker.

Additionally, with Shadow Mode, you can now compare the performance of your current model vs the candidate using real production data, before deploying the model to production.

Once you are ready to replace the model, you can now also generate a new change request in 1:1 Operations Manager, to ensure that the update follows standard revision management procedures.

Comparison of candidate vs current model

Comparison of candidate vs current model

For more information, see Updating active models in predictions.

  • Previous topic Increase the relevancy of real-time decisions with Next-Best-Action Customer Journeys (8.6)
  • Next topic Identify and Profile Underserved Customers with Value Finder (8.5)

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