Book Image

IBM Cloud Pak for Data

By : Hemanth Manda, Sriram Srinivasan, Deepak Rangarao
3 (1)
Book Image

IBM Cloud Pak for Data

3 (1)
By: Hemanth Manda, Sriram Srinivasan, Deepak Rangarao

Overview of this book

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.
Table of Contents (17 chapters)
Section 1: The Basics
Section 2: Product Capabilities
Section 3: Technical Details

Automating the AI life cycle using Cloud Pak for Data

After an organization has been able to collect its data and organize it using a trusted governance catalog, it can now tap into the data to build and scale AI models across the business. To build AI models from the ground up and scale it across the business, organizations need capabilities covering the full AI life cycle, and this includes the following:

  • Build—This is where companies build their AI models.
  • Run—After a model has been built, it needs to be put into production within an application or a business process.
  • Manage—After a model is built and running, the question becomes: How can it be scaled with trust and transparency? To address complex build and run environments, enterprises need a tool that not only manages the environment but also explains how their models arrived at their predictions.

Let's exercise Model Operations (ModelOps) using Cloud Pak for Data to understand...