Expert Product Reviews - Artificial Intelligence

We are pleased to share a comprehensive review of "The AI Value Playbook", published by Packt, and written by the reviewer Tanmay Gaur. This review offers an in-depth exploration of the book's key themes and insights, providing readers with a thorough understanding of its value.Please find the review below:As someone who has had the privilege of working closely on this book, I can confidently say that it is an invaluable resource for both newcomers to AI and seasoned professionals. The book offers a wealth of knowledge through interviews and case studies with industry experts, detailing their approaches to overcoming implementation challenges. Artificial intelligence (AI) stands as a disruptive and transformative force, reshaping how enterprises interact with customers and helping them streamline their operations. Embracing this technological revolution, most companies are now pivoting towards innovative AI-driven solutions that create new value streams for these organizations. For those early in their AI journey, this book provides a clear understanding of various AI use-cases applicable to enterprises and offers practical guidance on how to approach them. It serves as a foundational text that helps readers grasp the complexities and opportunities within the AI landscape.  For AI professionals further along in their careers, the book identifies key traits that contribute to successful AI implementations. Drawing from my personal experience with multiple AI initiatives, I found that the book affirmed my insights on what differentiates a successful project from a failure. It offers a nuanced perspective that is both enlightening and practical.  As the MTS for customer care at T-Mobile, I have  led multiple AI initiatives. One of the first initiatives was a recommendation engine based pilot in customer care, a form of predictive care we called Next Best Action. The case studies provided mirrored some of the challenges that we had to overcome in making that initiative successful.  I will cite a few examples below. A major challenge was the dispersed nature of data across various enterprise systems. Predicting the data needs for the solution and ensuring that all necessary data are sanitized and available in near real-time for model training and execution were critical hurdles. Another significant challenge was ensuring compliance with multiple data privacy laws. Additionally, establishing control groups to validate the solution’s results quickly and in iterations is a complex task. As an example, in customer care, the creation of control and treatment groups that are similar in all aspects to minimize the impact of external confounding variables required more effort than initially anticipated. A frequently overlooked challenge in AI implementations of recommendation engines turns out to be the actual presentation of dynamic AI/ML-driven insights to consumers of the data in a meaningful way, be it customer service agents or end customers. There are challenges both in the integration of these dynamic insights into the experiences as well as getting enough adoption to be successful. What I learned from iterations of experiments and at times failures, this book encapsulates that  knowledge in a succinct and easy-to-process package and thus provides a really unique value proposition. I highly recommend this book to anyone involved in the field of AI, whether you are just starting out or have years of experience. It is a comprehensive guide that will undoubtedly enhance your understanding and execution of AI projects. Reviewer BioTanmaya Gaur is a Principal Architect at T-Mobile US, Inc., with over 15 years of experience building enterprise systems. He is a technical expert in the architecture, development, and deployment of advanced software and infrastructure for enhanced user support in telecom applications. He is passionate about utilizing composable architecture strategies to aid the creation and management of reusable components across the entire spectrum of web development, from front-end UX to backend coordination and content management. His current focus is building Telecom CRM tools that leverage micro-front-end, artificial intelligence, and machine learning algorithms to boost efficiency and improve the product experience.

We are pleased to share a comprehensive review of "Google Machine Learning and Generative AI for Solutions Architects", published by Packt, and written by Sourav Kundu. This review offers an in-depth exploration of the book's key themes and insights, providing readers with a thorough understanding of its value.Please find the review below:Google Machine Learning and Generative AI for Solutions Architects" provides an introduction to foundational AI/ML concepts and Google Cloud's tools, guiding readers through practical applications, custom model building, and data preparation techniques. It covers model deployment, and MLOps practices and addresses fairness, bias, and explainability in AI models. The book concludes with a comprehensive overview of generative AI, including its evolution, applications, and advanced techniques.A few important topics of the book that I want to highlight are as follows:The book begins with an introduction to foundational AI/ML concepts and explores various real-world applications and challenges, laying the groundwork for understanding more advanced topics in the book along with explaining the ML Model Development Life Cycle.Next, it provides an overview of setting up and utilizing Google Cloud AI/ML services, including an introduction to the platform's tools and capabilities.It then focuses on practical applications of high-level AI services for common tasks such as image recognition and sentiment analysis​.The book guides readers through building custom machine learning models on Google Cloud, using popular libraries like scikit-learn along with Vertex AI.It further covers data preparation techniques for AI/ML, including building both batch and streaming data pipelines on Google Cloud, and discusses techniques for feature engineering and dimensionality reduction, highlighting tools such as PCA, LDA, and the Vertex AI Feature Store​The book then explores the concept of hyperparameters and strategies for hyperparameter optimization, providing hands-on examples with Vertex AI​ and also introduces neural networks and deep learning concepts, including model implementation in TensorFlow and challenges in optimizing neural networks.The book covers deployment strategies, monitoring, and scaling models in production environments, including A/B testing and edge optimization, and discusses the principles of MLOps (Machine Learning Operations) and how to implement them using tools like Vertex AI Pipelines for efficient model management​.It then examines critical issues around bias, fairness, and explainability in AI models, as well as the importance of lineage in tracking model development,​​ and focuses on governance practices and the architecture framework necessary for managing AI/ML workloads on Google Cloud.Finally, the book covers the concepts and techniques of generative AI, discussing its evolution and applications along with more advanced generative AI techniques, and providing insights into state-of-the-art models and their practical uses​.