دانلود کتاب IBM Cloud Pak for Data: An enterprise platform to operationalize data, analytics, and AI

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Cover\nTitle Page\nCopyright and Credits\nContributors\nAbout the reviewers\nTable of Contents\nPreface\nSection 1: The Basics\nChapter 1: The AI Ladder – IBM\'s Prescriptive Approach\n Market dynamics and IBM\'s Data and AI portfolio\n Introduction to the AI ladder\n The rungs of the AI ladder\n Collect – making data simple and accessible\n Organize – creating a trusted analytics foundation\n People empowering your data citizens\n Analyze – building and scaling models with trust and transparency\n Infuse – operationalizing AI throughout the business\n Customer service\n Risk and compliance\n IT operations\n Financial operations\n Business operations\n The case for a data and AI platform\n Summary\nChapter 2: Cloud Pak for Data: A Brief Introduction\n The case of a data and AI platform – recap\n Overview of Cloud Pak for Data\n Exploring unique differentiators, key use cases, and customer adoption\n Key use cases\n Customer use case: AI claim processing\n Customer use case: data and AI platform\n Cloud Pak for Data: additional details\n An open ecosystem\n Premium IBM cartridges and third-party services\n Industry accelerators\n Packaging and deployment options\n Red Hat OpenShift\n Summary\nSection 2: Product Capabilities\nChapter 3: Collect – Making Data Simple and Accessible\n Data – the world\'s most valuable asset\n Data-centric enterprises\n Challenges with data-centric delivery\n Enterprise data architecture\n NoSQL data stores – key categories\n Data virtualization – accessing data anywhere\n Data virtualization versus ETL – when to use what?\n Platform connections – streamlining data connectivity\n Data estate modernization using Cloud Pak for Data\n Summary\nChapter 4: Organize – Creating a Trusted Analytics Foundation\n Introducing Data Operations (DataOps)\n Organizing enterprise information assets\n Establishing metadata and stewardship\n Business metadata components\n Technical metadata components\n Profiling to get a better understanding of your data\n Classifying data for completeness\n Automating data discovery and business term assignment\n Enabling trust with data quality\n Steps to assess data quality\n DataOps in action\n Automation rules around data quality\n Data privacy and activity monitoring\n Data integration at scale\n Considerations for selecting a data integration tool\n The extract, transform, and load (ETL) service in Cloud Pak for Data\n Advantages of leveraging a cloud-native platform for ETL\n Master data management\n Extending MDM toward a Digital Twin\n Summary\nChapter 5: Analyzing: Building, Deploying, and Scaling Models with Trust and Transparency\n Self-service analytics of governed data\n BI and reporting\n Predictive versus prescriptive analytics\n Understanding AI\n AI life cycle – Transforming insights into action\n AI governance: Trust and transparency\n Automating the AI life cycle using Cloud Pak for Data\n Data science tools for a diverse data science team\n Distributed AI\n Establishing a collaborative environment and building AI models\n Choosing the right tools to use\n ModelOps – Deployment phase\n ModelOps – Monitoring phase\n Streaming data/analytics\n Distributed processing\n Summary\nChapter 6: Multi-Cloud Strategy and Cloud Satellite\n IBM\'s multi-cloud strategy\n Supported deployment options\n Managed OpenShift\n AWS Quick Start\n Azure Marketplace and quickstart templates\n Cloud Pak for Data as a Service\n Packaging and pricing\n IBM Cloud Satellite\n A data fabric for a multi-cloud future\n Summary\nChapter 7: IBM and Partner Extension Services\n IBM and third-party extension services\n Collect extension services\n Db2 Advanced\n Informix\n Virtual Data Pipeline\n EDB Postgres Advanced Server\n MongoDB Enterprise Advanced\n Organize extension services\n DataStage\n Information Server\n Master Data Management\n Analyze cartridges – IBM Palantir\n Infuse cartridges\n Cognos Analytics\n Planning Analytics\n Watson Assistant\n Watson Discovery\n Watson API Kit\n Modernization upgrades to Cloud Pak for Data cartridges\n Extension services\n Summary\nChapter 8: Customer Use Cases\n Improving health advocacy program efficiency\n Voice-enabled chatbots\n Risk and control automation\n Enhanced border security\n Unified Data Fabric\n Financial planning and analytics\n Summary\nSection 3: Technical Details\nChapter 9: Technical Overview, Management, and Administration\n Technical requirements\n Architecture overview\n Characteristics of the platform\n Technical underpinnings\n The operator pattern\n The platform technical stack\n Infrastructure requirements, storage, and networking\n Understanding how storage is used\n Networking\n Foundational services and the control plane\n Cloud Pak foundational services\n Cloud Pak for Data control plane\n Management and monitoring\n Multi-tenancy, resource management, and security\n Isolation using namespaces\n Resource management and quotas\n Enabling tenant self-management\n Day 2 operations\n Upgrades\n Scale-out\n Backup and restore\n Summary\n References\nChapter 10: Security and Compliance\n Technical requirements\n Security and Privacy by Design\n Development practices\n Vulnerability detection\n Delivering security assured container images\n Secure operations in a shared environment\n Securing Kubernetes hosts\n Security in OpenShift Container Platform\n Namespace scoping and service account privileges\n RBAC and the least privilege principle\n Workload notification and reliability assurance\n Additional considerations\n Encryption in motion and securing entry points\n Encryption at rest\n Anti-virus software\n User access and authorizations\n Authentication\n Authorization\n User management and groups\n Securing credentials\n Meeting compliance requirements\n Configuring the operating environment for compliance\n Auditing\n Integration with IBM Security Guardium\n Summary\n References\nChapter 11: Storage\n Understanding the concept of persistent volumes\n Kubernetes storage introduction\n Types of persistent volumes\n In-cluster storage\n Optimized hyperconverged storage and compute\n Separated compute and storage Nodes\n Provisioning procedure summary\n Off-cluster storage\n NFS-based persistent volumes\n Operational considerations\n Continuous availability with in-cluster storage\n Data protection – snapshots, backups, and active-passive disaster recovery\n Quiescing Cloud Pak for Data services\n Db2 database backups and HADR\n Kubernetes cluster backup and restore\n Summary\n Further reading\nChapter 12: Multi-Tenancy\n Tenancy considerations\n Designating tenants\n Organizational and operational implications\n Architecting for multi-tenancy\n Achieving tenancy with namespace scoping\n Ensuring separation of duties with Kubernetes RBAC and separation of duties with operators\n Securing access to a tenant instance\n Choosing dedicated versus shared compute nodes\n Reviewing the tenancy requirements\n Isolating tenants\n Tenant security and compliance\n Self-service and management\n A summary of the assessment\n In-namespace sub-tenancy with looser isolation\n Approach\n Assessing the limitations of this approach\n Summary\nOther Books You May Enjoy\nIndex