IBM Cloud Pak for Data

Hemanth Manda, Sriram Srinivasan, Deepak Rangarao

• Editore: Packt Publishing
• Anno: 2021
• ISBN:
  9781800567405
• Edizione: 1
• Argomento: Computer Science
• Categoria: Data Modelling & Design

Build end-to-end AI solutions with IBM Cloud Pak for Data to operationalize AI on a secure platform based on cloud-native reliability, cost-effective multitenancy, and efficient resource managementKey Features• Explore data virtualization by accessing data in real time without moving it• Unify the data and AI experience with the integrated end-to-end platform• Explore the AI life cycle and learn to build, experiment, and operationalize trusted AI at scaleBook DescriptionCloud 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.What you will learn• Understand the importance of digital transformations and the role of data and AI platforms• Get to grips with data architecture and its relevance in driving AI adoption using IBM's AI Ladder• Understand Cloud Pak for Data, its value proposition, capabilities, and unique differentiators• Delve into the pricing, packaging, key use cases, and competitors of Cloud Pak for Data• Use the Cloud Pak for Data ecosystem with premium IBM and third-party services• Discover IBM's vibrant ecosystem of proprietary, open-source, and third-party offerings from over 35 ISVsWho this book is forThis book is for data scientists, data stewards, developers, and data-focused business executives interested in learning about IBM's Cloud Pak for Data. Knowledge of technical concepts related to data science and familiarity with data analytics and AI initiatives at various levels of maturity are required to make the most of this book.

Section 1: The Basics

In this section, we will learn about market trends, data and AI, IBM's offering portfolio, its prescriptive approach to AI adoption, and an overview of Cloud Pak for Data.

This section comprises the following chapters:

• Chapter 1, The AI Ladder: IBM's Prescriptive Approach
• Chapter 2, Cloud Pak for Data – A Brief Introduction

Chapter 1: The AI Ladder – IBM's Prescriptive Approach

Digital transformation is impacting every industry and business, with data and artificial intelligence (AI) playing a prominent role. For example, some of the largest companies in the world, such as Amazon, Facebook, Uber, and Google, leverage data and AI as a key differentiator. However, not every enterprise is successful in embracing AI and monetizing their data. The AI ladder is IBM's response to this market need – it's a prescriptive approach to AI adoption and entails four simple steps or rungs of the ladder.

In this chapter, you will learn about market dynamics, IBM's Data and AI portfolio, and a detailed overview of the AI ladder. We are also going to cover what it entails and how IBM offerings map to the different rungs of the ladder.

In this chapter, we will be covering the following main topics:

• Market dynamics and IBM's Data and AI portfolio
• Introduction to the AI ladder
• Collect – making data simple and accessible
• Organize – creating a trusted analytics foundation
• Analyze – building and scaling AI with trust and transparency
• Infuse – operationalizing AI throughout the business

Market dynamics and IBM's Data and AI portfolio

The fact is that every company in the world today is a data company. As the Economist magazine rightly pointed out in 2017, data is the world's most valuable resource and unless you are leveraging your data as a strategic differentiator, you are likely missing out on opportunities.

Simply put, data is the fuel, the cloud is the vehicle, and AI is the destination. The intersection of these three pillars of IT is the driving force behind digital transformation disrupting every company and industry. To be successful, companies need to quickly modernize their portfolio and embrace an intentional strategy to re-tool their data, AI, and application workloads by leveraging a cloud-native architecture. So, cloud platforms act as a great enabler by infusing agility, while AI is the ultimate destination, the so-called nirvana that every enterprise seeks to master.

While the benefits of the cloud are becoming obvious by the day, there are still several enterprises that are reluctant to embrace the public cloud right away. These enterprises are, in some cases, constrained by regulatory concerns, which make it a challenge to operate on public clouds. However, this doesn't mean that they don't see the value of the cloud and the benefits derived from embracing the cloud architecture. Everyone understands that the cloud is the ultimate destination, and taking the necessary steps to prepare and modernize their workloads is not an option, but a survival necessity:

Figure 1.1 – What's reshaping how businesses operate? The driving forces behind digital transformation

IBM enjoys a strong Data and AI portfolio, with 100+ products being developed and acquired over the past 40 years, including some marquee offerings such as Db2, Informix, DataStage, Cognos Analytics, SPSS Modeler, Planning Analytics, and more. The depth and breadth of IBM's portfolio is what makes it stand out in the market. With Cloud Pak for Data, IBM is doubling down on this differentiation, further simplifying and modernizing its portfolio as customers look to a hybrid, multi-cloud future.

Introduction to the AI ladder

We all know data is the foundation for businesses to drive smarter decisions. Data is what fuels digital transformation. But it is AI that unlocks the value of that data, which is why AI is poised to transform businesses with the potential to add almost 16 trillion dollars to the global economy by 2030. You can find the relevant source here: https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html.

However, the adoption of AI has been slower than anticipated. This is because many enterprises do not make a conscious effort to lay the necessary data foundation and invest in nurturing talent and business processes that are critical for success. For example, the vast majority of AI failures are due to data preparation and organization, not the AI models themselves. Success with AI models is dependent on achieving success in terms of how you collect and organize data. Business leaders not only need to understand the power of AI but also how they can fully unleash its potential and operate in a hybrid, multi-cloud world.

This section aims to demystify AI, common AI challenges and failures, and provide a unified, prescriptive approach (which we call "the AI ladder") to help organizations unlock the value of their data and accelerate their journey to AI.

As companies look to harness the potential of AI and identify the best ways to leverage data for business insights, they need to ensure that they start with a clearly defined business problem. In addition, you need to use data from diverse sources, support best-in-class tools and frameworks, and run models across a variety of environments.

According to a study by MIT Sloan Management Review, 81% of business leaders (http://marketing.mitsmr.com/offers/AI2017/59181-MITSMR-BCG-Report-2017.pdf) do not understand the data and infrastructure required for AI and "No amount of AI algorithmic sophistication will overcome a lack of data [architecture] – bad data is simply paralyzing."

Put simply: There is no AI without IA (information architecture).

IBM recognizes this challenge our clients are facing. As a result, IBM built a prescriptive approach (known as the AI ladder) to help clients with the aforementioned challenges and accelerate their journey to AI, no matter where they are on their journey. It allows them to simplify and automate how organizations turn data into insights by unifying the collection, organization, and analysis of data, regardless of where it lives. By climbing the AI ladder, enterprises can build a governed, efficient, agile, and future-proof approach to AI. Furthermore, it is also an organizing construct that underpins the Data and AI product portfolio of IBM.

It is critical to remember that AI is not magic and requires a thoughtful and well-architected approach. Every step of the ladder is critical to being successful with AI.

The rungs of the AI ladder

The following diagram illustrates IBM's prescriptive approach, also known as the AI ladder:

Figure 1.2 – The AI ladder – a prescriptive approach to the journey of AI

The AI ladder has four steps (often referred to as the rungs of the ladder). They are as follows:

1. Collect: Make data simple and accessible. Collect data of every type regardless of where it lives, enabling flexibility in the face of ever-changing data sources.
2. Organize: Create a business-ready analytics foundation. Organize all the client's data into a trusted, business-ready foundation with built-in governance, quality, protection, and compliance.
3. Analyze: Build and scale AI with trust and explainability. Analyze the client's data in smarter ways and benefit from AI models that empower the client's team to gain new insights and make better, smarter decisions.
4. Infuse: Operationalize AI throughout the business. You should do this across multiple departments and within various processes by drawing on predictions, automation, and optimization. Craft an effective AI strategy to realize your AI business objectives. Apply AI to automate and optimize existing workflows in your business, allowing your employees to focus on higher-value work.

Spanning the four steps of the AI ladder is the concept of Modernize from IBM, which allows clients to simplify and automate how they turn data into insights. It unifies collecting, organizing, and analyzing data within a multi-cloud data platform known as Cloud Pak for Data.

IBM's approach starts with a simple idea: run anywhere. This is because the platform can be deployed on the customer's infrastructure of choice. IBM supports Cloud Pak for Data deployments on every maj...