Optimized inferencing and integration with AI on IBM zSystems introduction, methodology, and use cases
In today's fast-paced, ever-growing digital world, you face various new and complex business problems. To help resolve these problems, enterprises are embedding artificial intelligence (AI) into their mission-critical business processes and applications to help improve operations, optimize perf...
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| 520 | |a In today's fast-paced, ever-growing digital world, you face various new and complex business problems. To help resolve these problems, enterprises are embedding artificial intelligence (AI) into their mission-critical business processes and applications to help improve operations, optimize performance, personalize the user experience, and differentiate themselves from the competition. Furthermore, the use of AI on the IBM℗ʼ zSystems platform, where your mission-critical transactions, data, and applications are installed, is a key aspect of modernizing business-critical applications while maintaining strict service-level agreements (SLAs) and security requirements. This colocation of data and AI empowers your enterprise to optimally and easily deploy and infuse AI capabilities into your enterprise workloads with the most recent and relevant data available in real time, which enables a more transparent, accurate, and dependable AI experience. This IBM Redpaper publication introduces and explains AI technologies and hardware optimizations, such as IBM zSystems Integrated Accelerator for AI, and demonstrates how to leverage certain capabilities and components to enable solutions in business-critical use cases, such as fraud detection and credit risk scoring on the platform. Real-time inferencing with AI models, a capability that is critical to certain industries and use cases such as fraud detection, now can be implemented with optimized performance thanks to innovations like IBM zSystems Integrated Accelerator for AI embedded in the Telum chip within IBM z16⁴́Ø. This publication also describes and demonstrates the implementation and integration of the two end-to-end solutions (fraud detection and credit risk), from developing and training the AI models to deploying the models in an IBM z/OS℗ʼ V2R5 environment on IBM z16 hardware, and to integrating AI functions into an application, for example an IBM z/OS Customer Information Control System (IBM CICS℗ʼ) application. We describe performance optimization recommendations and considerations when leveraging AI technology on the IBM zSystems platform, including optimizations for micro-batching in IBM Watson℗ʼ Machine Learning for z/OS (WMLz). The benefits that are derived from the solutions also are described in detail, which includes how the open-source AI framework portability of the IBM zSystems platform enables model development and training to be done anywhere, including on IBM zSystems, and the ability to easily integrate to deploy on IBM zSystems for optimal inferencing. You can uncover insights at the transaction level while taking advantage of the speed, depth, and securability of the platform. This publication is intended for technical specialists, site reliability engineers, architects, system programmers, and systems engineers. Technologies that are covered include TensorFlow Serving, WMLz, IBM Cloud Pak℗ʼ for Data (CP4D), IBM z/OS Container Extensions (zCX), IBM Customer Information Control System (IBM CICS), Open Neural Network Exchange (ONNX), and IBM Deep Learning Compiler (zDLC). | ||
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| spelling | Manna, Makenzie VerfasserIn aut Optimized inferencing and integration with AI on IBM zSystems introduction, methodology, and use cases Makenzie Manna, Erhan Mengusoglu, Artem Minin, Krishna Teja Rekapalli, Thomas Rüter, Pia Velazco, Markus Wolff [First edition]. [Redmond, Washington] IBM 2022 1 online resource (128 pages) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IBM redbooks In today's fast-paced, ever-growing digital world, you face various new and complex business problems. To help resolve these problems, enterprises are embedding artificial intelligence (AI) into their mission-critical business processes and applications to help improve operations, optimize performance, personalize the user experience, and differentiate themselves from the competition. Furthermore, the use of AI on the IBM℗ʼ zSystems platform, where your mission-critical transactions, data, and applications are installed, is a key aspect of modernizing business-critical applications while maintaining strict service-level agreements (SLAs) and security requirements. This colocation of data and AI empowers your enterprise to optimally and easily deploy and infuse AI capabilities into your enterprise workloads with the most recent and relevant data available in real time, which enables a more transparent, accurate, and dependable AI experience. This IBM Redpaper publication introduces and explains AI technologies and hardware optimizations, such as IBM zSystems Integrated Accelerator for AI, and demonstrates how to leverage certain capabilities and components to enable solutions in business-critical use cases, such as fraud detection and credit risk scoring on the platform. Real-time inferencing with AI models, a capability that is critical to certain industries and use cases such as fraud detection, now can be implemented with optimized performance thanks to innovations like IBM zSystems Integrated Accelerator for AI embedded in the Telum chip within IBM z16⁴́Ø. This publication also describes and demonstrates the implementation and integration of the two end-to-end solutions (fraud detection and credit risk), from developing and training the AI models to deploying the models in an IBM z/OS℗ʼ V2R5 environment on IBM z16 hardware, and to integrating AI functions into an application, for example an IBM z/OS Customer Information Control System (IBM CICS℗ʼ) application. We describe performance optimization recommendations and considerations when leveraging AI technology on the IBM zSystems platform, including optimizations for micro-batching in IBM Watson℗ʼ Machine Learning for z/OS (WMLz). The benefits that are derived from the solutions also are described in detail, which includes how the open-source AI framework portability of the IBM zSystems platform enables model development and training to be done anywhere, including on IBM zSystems, and the ability to easily integrate to deploy on IBM zSystems for optimal inferencing. You can uncover insights at the transaction level while taking advantage of the speed, depth, and securability of the platform. This publication is intended for technical specialists, site reliability engineers, architects, system programmers, and systems engineers. Technologies that are covered include TensorFlow Serving, WMLz, IBM Cloud Pak℗ʼ for Data (CP4D), IBM z/OS Container Extensions (zCX), IBM Customer Information Control System (IBM CICS), Open Neural Network Exchange (ONNX), and IBM Deep Learning Compiler (zDLC). IBM Power systems Artificial intelligence Intelligence artificielle artificial intelligence Mengusoglu, Erhan VerfasserIn aut Minin, Artem VerfasserIn aut Rekapalli, Krishna Teja VerfasserIn aut Rüter, Thomas VerfasserIn aut Velazco, Pia VerfasserIn aut Wolff, Markus VerfasserIn aut TUM01 ZDB-30-ORH TUM_PDA_ORH https://learning.oreilly.com/library/view/-/9780738460925/?ar X:ORHE Aggregator lizenzpflichtig Volltext |
| spellingShingle | Manna, Makenzie Mengusoglu, Erhan Minin, Artem Rekapalli, Krishna Teja Rüter, Thomas Velazco, Pia Wolff, Markus Optimized inferencing and integration with AI on IBM zSystems introduction, methodology, and use cases IBM Power systems Artificial intelligence Intelligence artificielle artificial intelligence |
| title | Optimized inferencing and integration with AI on IBM zSystems introduction, methodology, and use cases |
| title_auth | Optimized inferencing and integration with AI on IBM zSystems introduction, methodology, and use cases |
| title_exact_search | Optimized inferencing and integration with AI on IBM zSystems introduction, methodology, and use cases |
| title_full | Optimized inferencing and integration with AI on IBM zSystems introduction, methodology, and use cases Makenzie Manna, Erhan Mengusoglu, Artem Minin, Krishna Teja Rekapalli, Thomas Rüter, Pia Velazco, Markus Wolff |
| title_fullStr | Optimized inferencing and integration with AI on IBM zSystems introduction, methodology, and use cases Makenzie Manna, Erhan Mengusoglu, Artem Minin, Krishna Teja Rekapalli, Thomas Rüter, Pia Velazco, Markus Wolff |
| title_full_unstemmed | Optimized inferencing and integration with AI on IBM zSystems introduction, methodology, and use cases Makenzie Manna, Erhan Mengusoglu, Artem Minin, Krishna Teja Rekapalli, Thomas Rüter, Pia Velazco, Markus Wolff |
| title_short | Optimized inferencing and integration with AI on IBM zSystems |
| title_sort | optimized inferencing and integration with ai on ibm zsystems introduction methodology and use cases |
| title_sub | introduction, methodology, and use cases |
| topic | IBM Power systems Artificial intelligence Intelligence artificielle artificial intelligence |
| topic_facet | IBM Power systems Artificial intelligence Intelligence artificielle artificial intelligence |
| url | https://learning.oreilly.com/library/view/-/9780738460925/?ar |
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