Ibm Spectrum Discover
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Ibm Spectrum Discover Metadata Management For Deep Insight Of Unstructured Storage
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Author : Joseph Dain
language : en
Publisher: IBM Redbooks
Release Date : 2019-10-01
Ibm Spectrum Discover Metadata Management For Deep Insight Of Unstructured Storage written by Joseph Dain and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-01 with Computers categories.
This IBM® Redpaper publication provides a comprehensive overview of the IBM Spectrum® Discover metadata management software platform. We give a detailed explanation of how the product creates, collects, and analyzes metadata. Several in-depth use cases are used that show examples of analytics, governance, and optimization. We also provide step-by-step information to install and set up the IBM Spectrum Discover trial environment. More than 80% of all data that is collected by organizations is not in a standard relational database. Instead, it is trapped in unstructured documents, social media posts, machine logs, and so on. Many organizations face significant challenges to manage this deluge of unstructured data such as: Pinpointing and activating relevant data for large-scale analytics Lacking the fine-grained visibility that is needed to map data to business priorities Removing redundant, obsolete, and trivial (ROT) data Identifying and classifying sensitive data IBM Spectrum Discover is a modern metadata management software that provides data insight for petabyte-scale file and Object Storage, storage on premises, and in the cloud. This software enables organizations to make better business decisions and gain and maintain a competitive advantage. IBM Spectrum Discover provides a rich metadata layer that enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of unstructured data. It improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research.
Making Data Smarter With Ibm Spectrum Discover Practical Ai Solutions
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Author : Ivaylo B. Bozhinov
language : en
Publisher: IBM Redbooks
Release Date : 2020-10-19
Making Data Smarter With Ibm Spectrum Discover Practical Ai Solutions written by Ivaylo B. Bozhinov and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-19 with Computers categories.
More than 80% of all data that is collected by organizations is not in a standard relational database. Instead, it is trapped in unstructured documents, social media posts, machine logs, and so on. Many organizations face significant challenges to manage this deluge of unstructured data, such as the following examples: Pinpointing and activating relevant data for large-scale analytics Lacking the fine-grained visibility that is needed to map data to business priorities Removing redundant, obsolete, and trivial (ROT) data Identifying and classifying sensitive data IBM® Spectrum Discover is a modern metadata management software that provides data insight for petabyte-scale file and Object Storage, storage on-premises, and in the cloud. This software enables organizations to make better business decisions and gain and maintain a competitive advantage. IBM Spectrum® Discover provides a rich metadata layer that enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of unstructured data. It improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research. This IBM Redbooks® publication presents several use cases that are focused on artificial intelligence (AI) solutions with IBM Spectrum Discover. This book helps storage administrators and technical specialists plan and implement AI solutions by using IBM Spectrum Discover and several other IBM Storage products.
Cataloging Unstructured Data In Ibm Watson Knowledge Catalog With Ibm Spectrum Discover
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Author : Joseph Dain
language : en
Publisher: IBM Redbooks
Release Date : 2020-08-11
Cataloging Unstructured Data In Ibm Watson Knowledge Catalog With Ibm Spectrum Discover written by Joseph Dain and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-11 with Computers categories.
This IBM® Redpaper publication explains how IBM Spectrum® Discover integrates with the IBM Watson® Knowledge Catalog (WKC) component of IBM Cloud® Pak for Data (IBM CP4D) to make the enriched catalog content in IBM Spectrum Discover along with the associated data available in WKC and IBM CP4D. From an end-to-end IBM solution point of view, IBM CP4D and WKC provide state-of-the-art data governance, collaboration, and artificial intelligence (AI) and analytics tools, and IBM Spectrum Discover complements these features by adding support for unstructured data on large-scale file and object storage systems on premises and in the cloud. Many organizations face challenges to manage unstructured data. Some challenges that companies face include: Pinpointing and activating relevant data for large-scale analytics, machine learning (ML) and deep learning (DL) workloads. Lacking the fine-grained visibility that is needed to map data to business priorities. Removing redundant, obsolete, and trivial (ROT) data and identifying data that can be moved to a lower-cost storage tier. Identifying and classifying sensitive data as it relates to various compliance mandates, such as the General Data Privacy Regulation (GDPR), Payment Card Industry Data Security Standards (PCI-DSS), and the Health Information Portability and Accountability Act (HIPAA). This paper describes how IBM Spectrum Discover provides seamless integration of data in IBM Storage with IBM Watson Knowledge Catalog (WKC). Features include: Event-based cataloging and tagging of unstructured data across the enterprise. Automatically inspecting and classifying over 1000 unstructured data types, including genomics and imaging specific file formats. Automatically registering assets with WKC based on IBM Spectrum Discover search and filter criteria, and by using assets in IBM CP4D. Enforcing data governance policies in WKC in IBM CP4D based on insights from IBM Spectrum Discover, and using assets in IBM CP4D. Several in-depth use cases are used that show examples of healthcare, life sciences, and financial services. IBM Spectrum Discover integration with WKC enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of data. The integration improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research.
Ibm Spectrum Discover
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Author : Joe Dain
language : en
Publisher:
Release Date : 2019
Ibm Spectrum Discover written by Joe Dain and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Database management categories.
Cataloging Unstructured Data In Ibm Watson Knowledge Catalog With Ibm Spectrum Discover
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Author : Joseph Dain
language : en
Publisher:
Release Date : 2020
Cataloging Unstructured Data In Ibm Watson Knowledge Catalog With Ibm Spectrum Discover written by Joseph Dain and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Database management categories.
Data Accelerator For Ai And Analytics
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Author : Simon Lorenz
language : en
Publisher: IBM Redbooks
Release Date : 2021-01-20
Data Accelerator For Ai And Analytics written by Simon Lorenz and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-20 with Computers categories.
This IBM® Redpaper publication focuses on data orchestration in enterprise data pipelines. It provides details about data orchestration and how to address typical challenges that customers face when dealing with large and ever-growing amounts of data for data analytics. While the amount of data increases steadily, artificial intelligence (AI) workloads must speed up to deliver insights and business value in a timely manner. This paper provides a solution that addresses these needs: Data Accelerator for AI and Analytics (DAAA). A proof of concept (PoC) is described in detail. This paper focuses on the functions that are provided by the Data Accelerator for AI and Analytics solution, which simplifies the daily work of data scientists and system administrators. This solution helps increase the efficiency of storage systems and data processing to obtain results faster while eliminating unnecessary data copies and associated data management.
Deployment And Usage Guide For Running Ai Workloads On Red Hat Openshift And Nvidia Dgx Systems With Ibm Spectrum Scale
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Author : Simon Lorenz
language : en
Publisher: IBM Redbooks
Release Date : 2020-11-30
Deployment And Usage Guide For Running Ai Workloads On Red Hat Openshift And Nvidia Dgx Systems With Ibm Spectrum Scale written by Simon Lorenz and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-30 with Computers categories.
This IBM® Redpaper publication describes the architecture, installation procedure, and results for running a typical training application that works on an automotive data set in an orchestrated and secured environment that provides horizontal scalability of GPU resources across physical node boundaries for deep neural network (DNN) workloads. This paper is mostly relevant for systems engineers, system administrators, or system architects that are responsible for data center infrastructure management and typical day-to-day operations such as system monitoring, operational control, asset management, and security audits. This paper also describes IBM Spectrum® LSF® as a workload manager and IBM Spectrum Discover as a metadata search engine to find the right data for an inference job and automate the data science workflow. With the help of this solution, the data location, which may be on different storage systems, and time of availability for the AI job can be fully abstracted, which provides valuable information for data scientists.
Ibm Reference Architecture For High Performance Data And Ai In Healthcare And Life Sciences
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Author : Dino Quintero
language : en
Publisher: IBM Redbooks
Release Date : 2019-09-08
Ibm Reference Architecture For High Performance Data And Ai In Healthcare And Life Sciences written by Dino Quintero and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-08 with Computers categories.
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.
Implementation Guide For Ibm Elastic Storage System 5000
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Author : Brian Herr
language : en
Publisher: IBM Redbooks
Release Date : 2020-12-08
Implementation Guide For Ibm Elastic Storage System 5000 written by Brian Herr and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-08 with Computers categories.
This IBM® Redbooks® publication introduces and describes the IBM Elastic Storage® Server 5000 (ESS 5000) as a scalable, high-performance data and file management solution. The solution is built on proven IBM Spectrum® Scale technology, formerly IBM General Parallel File System (IBM GPFS). ESS is a modern implementation of software-defined storage, making it easier for you to deploy fast, highly scalable storage for AI and big data. With the lightning-fast NVMe storage technology and industry-leading file management capabilities of IBM Spectrum Scale, the ESS 3000 and ESS 5000 nodes can grow to over YB scalability and can be integrated into a federated global storage system. By consolidating storage requirements from the edge to the core data center — including kubernetes and Red Hat OpenShift — IBM ESS can reduce inefficiency, lower acquisition costs, simplify storage management, eliminate data silos, support multiple demanding workloads, and deliver high performance throughout your organization. This book provides a technical overview of the ESS 5000 solution and helps you to plan the installation of the environment. We also explain the use cases where we believe it fits best. Our goal is to position this book as the starting point document for customers that would use the ESS 5000 as part of their IBM Spectrum Scale setups. This book is targeted toward technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for delivering cost-effective storage solutions with ESS 5000.
Ibm Power Systems Enterprise Ai Solutions
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Author : Scott Vetter
language : en
Publisher: IBM Redbooks
Release Date : 2019-09-25
Ibm Power Systems Enterprise Ai Solutions written by Scott Vetter and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-25 with Computers categories.
This IBM® Redpaper publication helps the line of business (LOB), data science, and information technology (IT) teams develop an information architecture (IA) for their enterprise artificial intelligence (AI) environment. It describes the challenges that are faced by the three roles when creating and deploying enterprise AI solutions, and how they can collaborate for best results. This publication also highlights the capabilities of the IBM Cognitive Systems and AI solutions: IBM Watson® Machine Learning Community Edition IBM Watson Machine Learning Accelerator (WMLA) IBM PowerAI Vision IBM Watson Machine Learning IBM Watson Studio Local IBM Video Analytics H2O Driverless AI IBM Spectrum® Scale IBM Spectrum Discover This publication examines the challenges through five different use case examples: Artificial vision Natural language processing (NLP) Planning for the future Machine learning (ML) AI teaming and collaboration This publication targets readers from LOBs, data science teams, and IT departments, and anyone that is interested in understanding how to build an IA to support enterprise AI development and deployment.