Data Science Mit Aws

Data Science mit AWS

Ecosystem Edge

To succeed in the face of disruptive competition, companies will need to harness the power of a wide range of partners who can bring different skills, experience, capacity, and their own networks to the task. With the advent of new technologies, rapidly changing customer needs, and emerging competitors, companies across more and more industries are seeing their time-honored ways of making money under threat. In this book, Arnoud De Meyer and Peter J. Williamson explain how business can meet these challenges by building a large and dynamic ecosystem of partners that reinforce, strengthen, and encourage innovation in the face of ongoing disruption. While traditional companies know how to assemble and manage supply chains, leading the development of a vibrant ecosystem requires a different set of capabilities. Ecosystem Edge illustrates how executives need to leave notions of command and control behind in favor of strategies that will attract partners, stimulate learning, and promote the overall health of the network. To understand the practical steps executives can take to achieve this, the authors focus on eight core examples that cross industries and continents: Alibaba Group, Amazon.com, ARM, athenahealth, Dassault Systèmes S.E., The Guardian, Rolls-Royce, and Thomson Reuters. By following the principles outlined in this book, leaders can learn how to unlock rapid innovation, tap into new and original sources of value, and practice organizational flexibility. As a result, companies can gain the ecosystem edge, a key advantage in responding to the challenges of disruption that business sees all around it today.

Data Analytics and AI

Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.