Skip to main content

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

Suggest edit 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.

Did you find this content helpful? YesNo

Have a question? Get answers now.

Visit the Collaboration Center to ask questions, engage in discussions, share ideas, and help others.

Ready to crush complexity?

Experience the benefits of Pega Community when you log in.

We'd prefer it if you saw us at our best.

Pega.com is not optimized for Internet Explorer. For the optimal experience, please use:

Close Deprecation Notice
Contact us