Kirjahaku

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.

Data warehouses and data lakes each evolved to meet a set of specific technology and business needs and values. As organizations often need both, there has been increasing demand for convergence of both technologies. Thus, the lakehouse was born. A lakehouse couples the cost benefits and versatility of data lakes with the data structure and high-performance data management capabilities of data warehouses into a single unified data store that can be consistently and efficiently accessed, governed, analyzed and consumed by AI applications. Lakehouses are designed to help organizations get more from their existing investment in data warehouses and data lakes. It supports the existence of both through access to and management of a larger variety of combined data for increased flexibility, enhancing business intelligence and AI initiatives by revealing deeper insights into an organization’s data estates. This book is intended for technical communities, such as developers, data scientists, and C-level IT executives, as well as business communities, such as business managers requiring self-service analytics / AI, and C-level business executives.

Many organizations recognize the value and benefits Artificial Intelligence (AI) can bring if implemented correctly. This topic is outlined in the authors’ previous book, Artificial Intelligence: Evolution and Revolution. A long-standing challenge that many organizations continue to face is preparing for AI and making sure that their data and assets are accessible, manageable, and governed and are of the right quality so that they can be consumed by new and existing AI applications in order to infuse AI across the enterprise to help drive smarter business outcomes. Over the years, numerous paradigms and efforts have attempted to address the complexities of managing sprawling and disparate data silos, but all seemed to have fallen short of their promises and expectations. Organizations need the flexibility to put their data and assets where it makes most business sense, whether that’ s on premises or in the private or public cloud. This book attempts to explain the concepts and values that a data fabric approach can deliver to both technical and business communities.