IBM reference architecture for high performance data and AI in healthcare and life sciences:
This IBM® Redpaper publication provides an update to the original description of IBM Reference Architecture for Genomics. This paper expands the reference architecture to cover all of the major vertical areas of healthcare and life sciences industries, such as genomics, imaging, and clinical and tra...
Gespeichert in:
| Beteiligte Personen: | , |
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| Körperschaft: | |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Poughkeepsie, NY
IBM Corporation, International Technical Support Organization
2019
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| Ausgabe: | First edition (September 2019). |
| Schriftenreihe: | IBM redpaper
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9780738456904/?ar |
| Zusammenfassung: | This IBM® Redpaper publication provides an update to the original description of IBM Reference Architecture for Genomics. This paper expands the reference architecture to cover all of the major vertical areas of healthcare and life sciences industries, such as genomics, imaging, and clinical and translational research. The architecture was renamed IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences to reflect the fact that it incorporates key building blocks for high-performance computing (HPC) and software-defined storage, and that it supports an expanding infrastructure of leading industry partners, platforms, and frameworks. The reference architecture defines a highly flexible, scalable, and cost-effective platform for accessing, managing, storing, sharing, integrating, and analyzing big data, which can be deployed on-premises, in the cloud, or as a hybrid of the two. IT organizations can use the reference architecture as a high-level guide for overcoming data management challenges and processing bottlenecks that are frequently encountered in personalized healthcare initiatives, and in compute-intensive and data-intensive biomedical workloads. This reference architecture also provides a framework and context for modern healthcare and life sciences institutions to adopt cutting-edge technologies, such as cognitive life sciences solutions, machine learning and deep learning, Spark for analytics, and cloud computing. To illustrate these points, this paper includes case studies describing how clients and IBM Business Partners alike used the reference architecture in the deployments of demanding infrastructures for precision medicine. This publication targets technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing life sciences solutions and support. |
| Beschreibung: | Number on back cover: REDP-5481-00. - Includes bibliographical references. - Online resource; title from cover (Safari, viewed October 11, 2019) |
| Umfang: | 1 Online-Ressource (1 volume) illustrations |
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| 520 | |a This IBM® Redpaper publication provides an update to the original description of IBM Reference Architecture for Genomics. This paper expands the reference architecture to cover all of the major vertical areas of healthcare and life sciences industries, such as genomics, imaging, and clinical and translational research. The architecture was renamed IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences to reflect the fact that it incorporates key building blocks for high-performance computing (HPC) and software-defined storage, and that it supports an expanding infrastructure of leading industry partners, platforms, and frameworks. The reference architecture defines a highly flexible, scalable, and cost-effective platform for accessing, managing, storing, sharing, integrating, and analyzing big data, which can be deployed on-premises, in the cloud, or as a hybrid of the two. IT organizations can use the reference architecture as a high-level guide for overcoming data management challenges and processing bottlenecks that are frequently encountered in personalized healthcare initiatives, and in compute-intensive and data-intensive biomedical workloads. This reference architecture also provides a framework and context for modern healthcare and life sciences institutions to adopt cutting-edge technologies, such as cognitive life sciences solutions, machine learning and deep learning, Spark for analytics, and cloud computing. To illustrate these points, this paper includes case studies describing how clients and IBM Business Partners alike used the reference architecture in the deployments of demanding infrastructures for precision medicine. This publication targets technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing life sciences solutions and support. | ||
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| spelling | Quintero, Dino VerfasserIn aut IBM reference architecture for high performance data and AI in healthcare and life sciences Dino Quintero, Frank N. Lee First edition (September 2019). Poughkeepsie, NY IBM Corporation, International Technical Support Organization 2019 1 Online-Ressource (1 volume) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IBM redpaper Number on back cover: REDP-5481-00. - Includes bibliographical references. - Online resource; title from cover (Safari, viewed October 11, 2019) This IBM® Redpaper publication provides an update to the original description of IBM Reference Architecture for Genomics. This paper expands the reference architecture to cover all of the major vertical areas of healthcare and life sciences industries, such as genomics, imaging, and clinical and translational research. The architecture was renamed IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences to reflect the fact that it incorporates key building blocks for high-performance computing (HPC) and software-defined storage, and that it supports an expanding infrastructure of leading industry partners, platforms, and frameworks. The reference architecture defines a highly flexible, scalable, and cost-effective platform for accessing, managing, storing, sharing, integrating, and analyzing big data, which can be deployed on-premises, in the cloud, or as a hybrid of the two. IT organizations can use the reference architecture as a high-level guide for overcoming data management challenges and processing bottlenecks that are frequently encountered in personalized healthcare initiatives, and in compute-intensive and data-intensive biomedical workloads. This reference architecture also provides a framework and context for modern healthcare and life sciences institutions to adopt cutting-edge technologies, such as cognitive life sciences solutions, machine learning and deep learning, Spark for analytics, and cloud computing. To illustrate these points, this paper includes case studies describing how clients and IBM Business Partners alike used the reference architecture in the deployments of demanding infrastructures for precision medicine. This publication targets technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing life sciences solutions and support. Information storage and retrieval systems Genomes Information storage and retrieval systems Life sciences Artificial intelligence Medical applications Information technology Management Systèmes d'information ; Sciences de la vie Intelligence artificielle en médecine Technologie de l'information ; Gestion Artificial intelligence ; Medical applications Information storage and retrieval systems ; Genomes Information storage and retrieval systems ; Life sciences Information technology ; Management Lee, Frank N. VerfasserIn aut International Business Machines Corporation. International Technical Support Organization, MitwirkendeR ctb |
| spellingShingle | Quintero, Dino Lee, Frank N. IBM reference architecture for high performance data and AI in healthcare and life sciences Information storage and retrieval systems Genomes Information storage and retrieval systems Life sciences Artificial intelligence Medical applications Information technology Management Systèmes d'information ; Sciences de la vie Intelligence artificielle en médecine Technologie de l'information ; Gestion Artificial intelligence ; Medical applications Information storage and retrieval systems ; Genomes Information storage and retrieval systems ; Life sciences Information technology ; Management |
| title | IBM reference architecture for high performance data and AI in healthcare and life sciences |
| title_auth | IBM reference architecture for high performance data and AI in healthcare and life sciences |
| title_exact_search | IBM reference architecture for high performance data and AI in healthcare and life sciences |
| title_full | IBM reference architecture for high performance data and AI in healthcare and life sciences Dino Quintero, Frank N. Lee |
| title_fullStr | IBM reference architecture for high performance data and AI in healthcare and life sciences Dino Quintero, Frank N. Lee |
| title_full_unstemmed | IBM reference architecture for high performance data and AI in healthcare and life sciences Dino Quintero, Frank N. Lee |
| title_short | IBM reference architecture for high performance data and AI in healthcare and life sciences |
| title_sort | ibm reference architecture for high performance data and ai in healthcare and life sciences |
| topic | Information storage and retrieval systems Genomes Information storage and retrieval systems Life sciences Artificial intelligence Medical applications Information technology Management Systèmes d'information ; Sciences de la vie Intelligence artificielle en médecine Technologie de l'information ; Gestion Artificial intelligence ; Medical applications Information storage and retrieval systems ; Genomes Information storage and retrieval systems ; Life sciences Information technology ; Management |
| topic_facet | Information storage and retrieval systems Genomes Information storage and retrieval systems Life sciences Artificial intelligence Medical applications Information technology Management Systèmes d'information ; Sciences de la vie Intelligence artificielle en médecine Technologie de l'information ; Gestion Artificial intelligence ; Medical applications Information storage and retrieval systems ; Genomes Information storage and retrieval systems ; Life sciences Information technology ; Management |
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