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IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI

(ebook) (audiobook) (audiobook) Książka w języku 1
IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI Hemanth Manda, Sriram Srinivasan, Deepak Rangarao - okladka książki

IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI Hemanth Manda, Sriram Srinivasan, Deepak Rangarao - okladka książki

IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI Hemanth Manda, Sriram Srinivasan, Deepak Rangarao - audiobook MP3

IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI Hemanth Manda, Sriram Srinivasan, Deepak Rangarao - audiobook CD

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336
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Do przechowalni

Cloud Pak for Data is IBM's modern data and AI platform that includes strategic offerings from its data and AI portfolio delivered in a cloud-native fashion with the flexibility of deployment on any cloud. The platform offers a unique approach to addressing modern challenges with an integrated mix of proprietary, open-source, and third-party services.
You'll begin by getting to grips with key concepts in modern data management and artificial intelligence (AI), reviewing real-life use cases, and developing an appreciation of the AI Ladder principle. Once you've gotten to grips with the basics, you will explore how Cloud Pak for Data helps in the elegant implementation of the AI Ladder practice to collect, organize, analyze, and infuse data and trustworthy AI across your business. As you advance, you'll discover the capabilities of the platform and extension services, including how they are packaged and priced. With the help of examples present throughout the book, you will gain a deep understanding of the platform, from its rich capabilities and technical architecture to its ecosystem and key go-to-market aspects.
By the end of this IBM book, you'll be able to apply IBM Cloud Pak for Data's prescriptive practices and leverage its capabilities to build a trusted data foundation and accelerate AI adoption in your enterprise.

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O autorach książki

Hemanth Manda heads product management at IBM and is responsible for the Cloud Pak for Data platform. He has broad experience in the technology and software industry spanning a number of strategy and execution roles over the past 20 years. In his current role, Hemanth leads a team of over 20 product managers responsible for simplifying and modernizing IBM's data and AI portfolio to support cloud-native architectures through the new platform offering that is Cloud Pak for Data. Among other things, he is responsible for rationalizing and streamlining the data and AI portfolio at IBM, a $6 billion-dollar business, and delivering new platform-wide capabilities through Cloud Pak for Data.

Sriram Srinivasan is an IBM Distinguished Engineer leading the architecture and development of Cloud Pak for Data. His interests lie in cloud-native technologies such as Kubernetes and their practical application for both client-managed environments and Software as a Service. Prior to this role, Sriram led the development of IBM Data Science Experience Local and the dashDB Warehouse as a Service for IBM Cloud. Early on in his career at IBM, Sriram led the development of various web and Eclipse tooling platforms, such as IBM Data Server Manager and the SQL Warehousing tool. He started his career at Informix, where he worked on application servers, database tools, e-commerce products, and Red Brick data warehouse.
Deepak Rangarao leads WW Technical Sales at IBM and is responsible for the Cloud Pak for Data platform. He has broad cross-industry experience in the data warehousing and analytics space, building analytic applications at large organizations and technical presales, both with start-ups and large enterprise software vendors. Deepak has co-authored several books on topics such as OLAP analytics, change data capture, data warehousing, and object storage and is a regular speaker at technical conferences. He is a certified technical specialist in Red Hat OpenShift, Apache Spark, Microsoft SQL Server, and web development technologies.

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