Explainable AI (XAI): Making Machine Learning Models Interpretable and Trustworthy Cloud Computing

Explainable AI (XAI): Making Machine Learning Models Interpretable and Trustworthy Cloud Computing
Author :
Publisher : Xoffencer international book publication house
Total Pages : 244
Release :
ISBN-10 : 9788197391538
ISBN-13 : 819739153X
Rating : 4/5 (53X Downloads)

Book Synopsis Explainable AI (XAI): Making Machine Learning Models Interpretable and Trustworthy Cloud Computing by : Amit Vyas

Download or read book Explainable AI (XAI): Making Machine Learning Models Interpretable and Trustworthy Cloud Computing written by Amit Vyas and published by Xoffencer international book publication house. This book was released on 2024-05-30 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Both explainable artificial intelligence (XAI) and cloud computing are vital components because they both play a significant part in the creation of the landscape of artificial intelligence (AI) and computing infrastructure. XAI and cloud computing are two of the most important pillars in the world of current technology. The purpose of this introduction is to provide an overview of the fundamental concepts behind both Explainable AI and cloud computing. In this section, we will study the relevance of these notions, as well as their applications and the synergies that they offer. A solution that satisfies the critical requirement for interpretability and transparency in artificial intelligence systems is referred to as explainable artificial intelligence, or XAI for short. Understanding the method by which artificial intelligence algorithms arrive at conclusions is of the highest significance, particularly in sensitive industries such as healthcare, finance, and law. This is because the algorithms are growing more intricate and prevalent, and it is becoming increasingly important to understand how they arrive at their results. XAI techniques are intended to give insights into the inner workings and reasoning processes of artificial intelligence models, with the purpose of demystifying the "black box" nature of these models. XAI approaches are aimed to deliver these insights. In addition to allowing stakeholders to detect biases or mistakes and ensure compliance with regulations, increasing the interpretability of artificial intelligence systems enables stakeholders to have a greater degree of trust in these systems. The provisioning, administration, and distribution of computer resources are all fundamentally transformed by cloud computing, which is regarded to be a breakthrough technology. Cloud computing is also known as utility computing. The term "cloud computing" refers to the practice of storing, managing, and processing data through the utilization of a network of distant servers that are located on the Internet. This is in contrast to the conventional method of computing, which is dependent on the infrastructure and servers located locally. This technology offers organizations unrivaled scalability, flexibility, and cost-efficiency, making it possible for them to use computer resources on demand without the trouble of managing physical infrastructure.


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