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|a UAMI
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|a Machine Learning with Business Rules on IBM Z :
|b Acting on Your Insights /
|c Chris Backhouse [and others].
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|a [Place of publication not identified]
|b IBM Redbooks,
|c 2019.
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|a 1 online resource
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|a text
|b txt
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|a Title from content provider.
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|a This Redpaper introduces the integration between two IBM products that you might like to consider when implementing a modern agile solution on your Z systems. The document briefly introduces Operational Decision Manager on z/OS and Machine learning on z/OS. In the case of Machine Learning we focus on the aspect of real-time scoring models and how these can be used with Business Rules to give better decisions. Note: Important changes since this document was written: This document was written for an older release of Operational Decision Manager for z/OS (ODM for z/OS). ODM for z/OS 8.9.1 required the writing of custom Java code to access a Watson Machine Learning for z/OS Scoring Service (this can be seen in). Since that time ODM for z/OS version 8.10.1 has been released and much improves the integration experience. Integrating the two products no longer requires custom Java code. Using ODM for z/OS 8.10.1 or later you can use an automated wizard in the ODM tooling to: Browse and select a model from Watson Machine Learning Import the Machine Learning data model into your rule project Automatically generate a template rule that integrates a call to the Watson Machine Learning scoring service Download and read this document for: Individual introductions to ODM for z/OS and Machine learning Discussions on the benefits of using the two technologies together Information on integrating if you have not yet updated to ODM for z/OS 8.10.1 For information about the machine learning integration in ODM for z/OS 8.10.1 see "IBM Watson Machine Learning for z/OS integration" topic in the ODM for z/OS 8.10.x Knowledge Center at: https://www.ibm.com/support/knowledgecenter/en/SSQP76_8.10.x/com.ibm.odm.zos.develop/topics/con_ml_integration_overview.html
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|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
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|a Backhouse, Chris.
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|t Online access: O'Reilly Media, Inc. O'Reilly Online Learning Platform: Academic edition (EZproxy Access)
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|z 0738456926
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|u https://learning.oreilly.com/library/view/~/9780738456928/?ar
|z Texto completo (Requiere registro previo con correo institucional)
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