The Ai Ladder Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of The Ai Ladder book. This book definitely worth reading, it is an incredibly well-written.
AI may be the greatest opportunity of our time, with the potential to add nearly $16 trillion to the global economy over the next decade. But so far adoption has been much slower than anticipated. With this practical report, business leaders will discover where they are in their AI journey and learn the steps they still need to take to implement an AI solution in their organization. Author Rob Thomas, general manager of data and AI at IBM, introduces C-suite executives and line-of-business professionals to the AI Ladder--a unified, prescriptive approach to help them accelerate their AI journey, no matter where they are in the process. This succinct report explores AI's drivers, value, and opportunity, as well as the adoption challenges organizations now face. Learn why you can't have AI without IA (information architecture) Unify data within a multicloud environment to unlock its value Make data accessible by collecting every type, regardless of where it lives Create a business-ready analytics foundation with built-in governance, protection, and compliance Benefit from AI models by learning smarter ways to analyze data Operationalize AI across multiple departments and within various processes
AI may be the greatest opportunity of our time, with the potential to add nearly $16 trillion to the global economy over the next decade. But so far, adoption has been much slower than anticipated, or so headlines may lead you to believe. With this practical guide, business leaders will discover where they are in their AI journey and learn the steps necessary to successfully scale AI throughout their organization. Authors Rob Thomas and Paul Zikopoulos from IBM introduce C-suite executives and business professionals to the AI Ladder—a unified, prescriptive approach to help them understand and accelerate the AI journey. Complete with real-world examples and real-life experiences, this book explores AI drivers, value, and opportunity, as well as the adoption challenges organizations face. Understand why you can’t have AI without an information architecture (IA) Appreciate how AI is as much a cultural change as it is a technological one Collect data and make it simple and accessible, regardless of where it lives Organize data to create a business-ready analytics foundation Analyze data, and build and scale AI with trust and transparency Infuse AI throughout your entire business and create intelligent workflows
AI may be the greatest opportunity of our time, with the potential to add nearly $16 trillion to the global economy over the next decade. But so far, adoption has been much slower than anticipated, or so headlines may lead you to believe. With this practical guide, business leaders will discover where they are in their AI journey and learn the steps necessary to successfully scale AI throughout their organization. Authors Rob Thomas and Paul Zikopoulos from IBM introduce C-suite executives and business professionals to the AI Ladder—a unified, prescriptive approach to help them understand and accelerate the AI journey. Complete with real-world examples and real-life experiences, this book explores AI drivers, value, and opportunity, as well as the adoption challenges organizations face. Understand why you can’t have AI without an information architecture (IA) Appreciate how AI is as much a cultural change as it is a technological one Collect data and make it simple and accessible, regardless of where it lives Organize data to create a business-ready analytics foundation Analyze data, and build and scale AI with trust and transparency Infuse AI throughout your entire business and create intelligent workflows
IBM Cloud Pak for Data by Hemanth Manda,Sriram Srinivasan,Deepak Rangarao Pdf
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 management Key FeaturesExplore data virtualization by accessing data in real time without moving itUnify the data and AI experience with the integrated end-to-end platformExplore the AI life cycle and learn to build, experiment, and operationalize trusted AI at scaleBook Description 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. What you will learnUnderstand the importance of digital transformations and the role of data and AI platformsGet to grips with data architecture and its relevance in driving AI adoption using IBM's AI LadderUnderstand Cloud Pak for Data, its value proposition, capabilities, and unique differentiatorsDelve into the pricing, packaging, key use cases, and competitors of Cloud Pak for DataUse the Cloud Pak for Data ecosystem with premium IBM and third-party servicesDiscover IBM's vibrant ecosystem of proprietary, open-source, and third-party offerings from over 35 ISVsWho this book is for This 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.
Smarter Data Science by Neal Fishman,Cole Stryker Pdf
Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how. Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.
Joseph Dain,Abeer Selim,Anil Patil,Christopher Vollmar,Flavio de Rezende,Frank Greco,Frank N. Lee,Isom Crawford Jr.,Ivaylo B. Bozhinov,Joanna Wong,Joshua Blumert,Larry Coyne,IBM Redbooks
Author : Joseph Dain,Abeer Selim,Anil Patil,Christopher Vollmar,Flavio de Rezende,Frank Greco,Frank N. Lee,Isom Crawford Jr.,Ivaylo B. Bozhinov,Joanna Wong,Joshua Blumert,Larry Coyne,IBM Redbooks Publisher : IBM Redbooks Page : 108 pages File Size : 55,9 Mb Release : 2020-08-11 Category : Computers ISBN : 9780738459028
Cataloging Unstructured Data in IBM Watson Knowledge Catalog with IBM Spectrum Discover by Joseph Dain,Abeer Selim,Anil Patil,Christopher Vollmar,Flavio de Rezende,Frank Greco,Frank N. Lee,Isom Crawford Jr.,Ivaylo B. Bozhinov,Joanna Wong,Joshua Blumert,Larry Coyne,IBM Redbooks Pdf
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.
Social Media Marketing For Dummies by Shiv Singh,Stephanie Diamond Pdf
Make friends and sell things to people through social media Social media technology is restlessly inventive, providing thousands of awesome ways for you to market your business inexpensively and on a large scale—often directly into the pockets of consumers. But in the proliferating, ever-changing world of tweets, influencers, handles, and alerts, it can be hard to know where to begin and then to evaluate what’s actually working for you. In the new edition of Social Media Marketing for Dummies, leading SMM voices Shiv Singh and Stephanie Diamond clear away the confusion and show you the smartest, most effective ways to plan, launch, manage, and assess your campaigns—and then iterate and optimize for increased success. Incorporating the latest trends and presented in a friendly, easily digestible step-by-step style, you’ll find the ultimate blueprint for developing your best SMM strategy. In no time, you’ll find out how to line up with Facebook, Twitter, Tumblr, and Google, develop a unique and compelling voice, and influence your key audience all the way to the bank. Choose the best SMM combination for you Avoid common mistakes and pitfalls Track your customers from awareness to retention Try out the latest stuff that really works Whether your organization is large or small, it simply doesn’t pay to be shy. Find your voice, get social, and chat your way to attracting and keeping new customers today!
Artificial Intelligence by Steven Astorino,Mark Simmonds Pdf
From humble evolutions in research papers and labs, artificial intelligence (AI)--which encompasses Machine Learning (ML) and Deep Learning (DL)--has matured in its many forms, infused in applications that can learn on their own and become progressively smarter with each interaction and transaction. Coupled with immense stores of data, maturity in CPU and GPU hardware, the invention of new, open source deep learning algorithms, and cloud technologies, operational AI has become available to the masses, setting the wheels in motion for a worldwide AI revolution that has never been seen before. This book attempts to help the reader on their AI journey by covering the concepts of AI, Machine Learning, and Deep Learning in its many forms; key ML and DL algorithms data scientists should learn; ethical challenges for the use of AI; how AI is being used across industries; possible future outlook for AI, and an AI Ladder to help accelerate the AI journey.
Many companies move workloads to the cloud only to encounter issues with legacy processes and organizational structures. How do you design new operating models for this environment? This practical book shows IT managers, CIOs, and CTOs how to address the hardest part of any cloud transformation: the people and the processes. Author Mike Kavis (Architecting the Cloud) explores lessons learned from enterprises in the midst of cloud transformations. Youâ??ll learn how to rethink your approach from a technology, process, and organizational standpoint to realize the promise of cost optimization, agility, and innovation that public cloud platforms provide. Learn the difference between working in a data center and operating in the cloud Explore patterns and anti-patterns for organizing cloud operating models Get best practices for making the organizational change required for a move to the cloud Understand why site reliability engineering is essential for cloud operations Improve organizational performance through value stream mapping
The book presents a detailed examination of many key aspects of philosophizing, ranging from the mission of philosophy, its method, its dialectic, and its epistemology. It conveys to the reader, student and scholar alike, a clear account of the aspirations and potential of the discipline. Readers will come away with a clearer understanding of the promise and challenges of philosophizing.
Much in the same way that software transformed business in the past two decades, AI is set to redefine organizations and entire industries. Just as every company is a software company today, every company will soon be an AI company. This practical guide explains how business and technical leaders can embrace this new breed of organization. Based on real customer experience, Microsoft’s David Carmona covers the journey necessary to become an AI Organization—from applying AI in your business today to the deep transformation that can empower your organization to redefine the industry. You'll learn the core concepts of AI as they are applied to real business, explore and prioritize the most appropriate use cases for AI in your company, and drive the organizational and cultural change needed to transform your business with AI.
Disruptive Artificial Intelligence and Sustainable Human Resource Management by Anamika Pandey,Balamurugan Balusamy,Naveen Chilamkurti Pdf
It is well-established that every organization is founded with a specific purpose and goals. Virtually all business organizations obtain measurements and metrics dealing with outcomes and results. However, what is reflected on the balance sheet does not necessarily show the actual worth of the organizations and ensure their survival and sustainability. Thus, the question arises, what exactly makes the organizations sustainable and thriving in an ever-changing environment? There are several examples of companies like Faber-Castell, Bank of Ireland, Chivas, DuPont and many more which have been able to sustain and survive over more than 200 years and still have a successful existence in the present era. Thus, it is important for the organization to pay equal attention to its three bottom lines, i.e., people, profit and planet for its sustainability, which not only meet the needs of the present generation but should have a futuristic approach as well. In the present era, organizations are adopting a new normal working dynamic and coping with the post-pandemic situations to sustain themselves in the business environment. Technology plays a significant role in this new working dynamic, and cutting-edge technologies like machine learning, artificial intelligence, blockchain,ChatGPT, Internet of Things, and much more effectively facilitate human functioning at work. The book aims to contribute to understanding and developing sustainable human resource management processes and practices aligned with recent technologies. It highlights the interconnection among the three bottom-line sustainable approaches for the growth and development of the organization. Topics discussed in this book include: • Disruptive Technologies and HR Sustainability • Convergence of Innovative Technologies, HR and Organizational Growth • Triple bottom-line Sustainability for HRM • Business-oriented Talent analytics and HR Sustainability • Role of Leadership in sustainable HRM practices.
A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence "Correlation is not causation." This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.
Explore why — now more than ever — the world is in a race to become data-driven, and how you can learn from examples of data-driven leadership in an Age of Disruption, Big Data, and AI In Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI, Fortune 1000 strategic advisor, noted author, and distinguished thought leader Randy Bean tells the story of the rise of Big Data and its business impact – its disruptive power, the cultural challenges to becoming data-driven, the importance of data ethics, and the future of data-driven AI. The book looks at the impact of Big Data during a period of explosive information growth, technology advancement, emergence of the Internet and social media, and challenges to accepted notions of data, science, and facts, and asks what it means to become "data-driven." Fail Fast, Learn Faster includes discussions of: The emergence of Big Data and why organizations must become data-driven to survive Why becoming data-driven forces companies to "think different" about their business The state of data in the corporate world today, and the principal challenges Why companies must develop a true "data culture" if they expect to change Examples of companies that are demonstrating data-driven leadership and what we can learn from them Why companies must learn to "fail fast and learn faster" to compete in the years ahead How the Chief Data Officer has been established as a new corporate profession Written for CEOs and Corporate Board Directors, data professional and practitioners at all organizational levels, university executive programs and students entering the data profession, and general readers seeking to understand the Information Age and why data, science, and facts matter in the world in which we live, Fail Fast, Learn Faster p;is essential reading that delivers an urgent message for the business leaders of today and of the future.
Digitalization in Healthcare by Patrick Glauner,Philipp Plugmann,Guido Lerzynski Pdf
Digital technologies are currently dramatically changing healthcare. This book introduces the reader to the latest digital innovations in healthcare in fields such as artificial intelligence, points out new ways in patient care and describes the limits of its application. It also offers essential guidance in the form of structured and authoritative contributions by domain experts spanning from artificial intelligence to hospital management to radiology to dentistry to preventive medicine. Furthermore, it shares ideas and experiences of industry veterans, in particular on how IT-driven solutions could solve long-standing issues in the fields of healthcare and hospitalization. It also gives advice on what new digital technologies to consider for becoming a healthcare market leader in the future. Taken together, these contributions provide a “road map” to guide decision makers, physicians, academics, industry representatives and other interested readers to understand the large impact of digital technology on healthcare today and its enormous potential for future development.