Optimized Inferencing And Integration With Ai On Ibm Zsystems Introduction Methodology And Use Cases
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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 performance, personalize the user experience, and differentiate themselves from the competition. Furthermore, the use of AI on the IBM® 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, and demonstrates how to leverage certain capabilities and components to enable AI 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, now can be implemented with optimized performance thanks to innovations like IBM zSystems Integrated Accelerator for AI embedded in the Telum chip within IBM z16TM. This publication 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® V2R5 environment on IBM z16 hardware, and integrating AI functions into an application, for example an IBM z/OS Customer Information Control System (IBM CICS®) 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® Machine Learning for z/OS. The benefits that are derived from the solutions also are described in detail, including 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 enables easy integration to deploy on IBM zSystems for optimal inferencing. Thus, allowing enterprises to 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® for Data (CP4D), IBM z/OS Container Extensions (zCX), IBM CICS, Open Neural Network Exchange (ONNX), and IBM Deep Learning Compiler (zDLC).
NANO-CHIPS 2030
In this book, a global team of experts from academia, research institutes and industry presents their vision on how new nano-chip architectures will enable the performance and energy efficiency needed for AI-driven advancements in autonomous mobility, healthcare, and man-machine cooperation. Recent reviews of the status quo, as presented in CHIPS 2020 (Springer), have prompted the need for an urgent reassessment of opportunities in nanoelectronic information technology. As such, this book explores the foundations of a new era in nanoelectronics that will drive progress in intelligent chip systems for energy-efficient information technology, on-chip deep learning for data analytics, and quantum computing. Given its scope, this book provides a timely compendium that hopes to inspire and shape the future of nanoelectronics in the decades to come.
Trust in Computer Systems and the Cloud
Learn to analyze and measure risk by exploring the nature of trust and its application to cybersecurity Trust in Computer Systems and the Cloud delivers an insightful and practical new take on what it means to trust in the context of computer and network security and the impact on the emerging field of Confidential Computing. Author Mike Bursell’s experience, ranging from Chief Security Architect at Red Hat to CEO at a Confidential Computing start-up grounds the reader in fundamental concepts of trust and related ideas before discussing the more sophisticated applications of these concepts to various areas in computing. The book demonstrates in the importance of understanding and quantifying risk and draws on the social and computer sciences to explain hardware and software security, complex systems, and open source communities. It takes a detailed look at the impact of Confidential Computing on security, trust and risk and also describes the emerging concept of trust domains, which provide an alternative to standard layered security. Foundational definitions of trust from sociology and other social sciences, how they evolved, and what modern concepts of trust mean to computer professionals A comprehensive examination of the importance of systems, from open-source communities to HSMs, TPMs, and Confidential Computing with TEEs. A thorough exploration of trust domains, including explorations of communities of practice, the centralization of control and policies, and monitoring Perfect for security architects at the CISSP level or higher, Trust in Computer Systems and the Cloud is also an indispensable addition to the libraries of system architects, security system engineers, and master’s students in software architecture and security.