Expert Product Reviews

We are pleased to share a comprehensive review of the "​Spring Boot 3.0 Cookbook", published by Packt, and written by the reviewer Erica Ayala. 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:I actually found this book before it was even released. I was searching for books on Java and Spring when I came across "Spring Boot 3.0 Cookbook", which wouldn't be released for a couple more months. 😩 I was so excited to get my hands on it that I literally set a reminder in my phone to order it the day it was released on Amazon. 😅When it finally arrived, I jumped right into reading about automated testing, mocking services, and implementing security protocols like OAuth2. (Not my favorite topics, to be honest, but they're my weakest areas and I'm trying to get better at them so 🤷🏽‍♀️, here we are. 😮‍💨😅)The book emphasizes the importance of automated testing over manual testing due to its scalability and efficiency. It covers the basics of unit and integration testing using Spring Boot's built-in testing starter. For the longest time, I didn't know the difference between the different types of testing (they're all just testing to me 🤷🏽‍♀️🥴), so this part was pretty helpful. 😅It also covers how to mock remote dependencies using Wiremock. The book explains how to configure Wiremock and integrate it with Spring Boot for testing, which I found pretty helpful because even though I've definitely created mocks for testing before, I used MockMVC to do it, so Wiremock was completely new to me. 🤯The security section is really detailed and covers setting up an Authorization Server, protecting APIs with OAuth2, and configuring different scopes and client authentication methods. It explains handling various OAuth2 flows, such as client credentials and authorization code grant flows.I found this part helpful since I'm still pretty new to OAuth2 and have mostly used JWT up until now. 😕 (Speaking of which, it also explains how to use JWT tokens for secure communication and validation. 😉)The book also mentions integration with social providers like GitHub and Facebook and even walks you through integrating Google Account login into your Spring Boot applications. Now this is the part I was excited about!! 🤩😁 By configuring your Authorization Server as an OAuth2 client with Google as the Identity Provider (IdP), you can enable users to log in with their Gmail accounts.It also explains how to configure the security chain using Spring Security's 'SecurityConfig' class, detailing the setup of 'Security FilterChain' beans to handle various security checks and the login process. This part was definitely helpful for me because 'Security FilterChain' was one of the things that my bootcamp just completely glossed over. 😕 It was kinda like "Paste these snippets of code into such-and-such class to implement security", and that was it. 🫤🤷🏽‍♀️There's a chapter where the book teaches you to identify performance bottlenecks and optimize your application. Now this stuff was completely new to me. 😳 Thankfully, it guides you through the entire process, from identifying issues to applying solutions like caching and runtime tuning.It clearly explains the impact caching has on overall performance. By reducing the load on the database through caching, the application can handle more requests efficiently, demonstrating the real-world benefits of proper caching strategies.The book also talks about monitoring and application health and covers metrics like CPU usage, heap memory, etc. explaining the importance of diagnosing and fixing performance issues.Overall, I think this book is a great resource for Java developers and it's packed with clear explanations and detailed examples to help you develop secure RESTful APIs with Spring Boot. 😊 I literally waited months for this book to be released and I can honestly say that it exceeded my expectations!! 😁

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 "Hands-On Genetic Algorithms with Python - Second Edition", published by Packt, and written by Ernest Namdar. 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:"Hands-On Genetic Algorithms with Python" by Eyal Wirsansky stands out as an exemplary resource for anyone eager to explore the world of Genetic Algorithms (GAs). Wirsansky has crafted a comprehensive guide that caters to a wide spectrum of needs, making it an invaluable asset whether you are a student, researcher, or educator. This book brilliantly balances theoretical foundations with practical applications, providing a clear and thorough exploration of GAs.The table of contents unfolds like pieces of a puzzle, fitting together seamlessly to reveal an impressive and coherent picture of GAs. The author has included a well-organized, meticulously documented, and accessible Python code repository. This hands-on approach empowers readers to gain practical experience, enabling them to apply the techniques to their own research and projects effectively.The fact that the book has reached its second edition is a testament to its success and wide acceptance in the field. Similar to the first edition, Part 3 is the highlight, where the intersection of GAs and Artificial Intelligence (AI) is explored in depth. Topics such as Feature Selection for Machine Learning (ML) models, Hyperparameter Tuning, Architecture Optimization of Deep Learning Networks, and Reinforcement Learning with GAs are comprehensively covered, continuing to build on the solid foundation laid in the previous edition.In this new edition, Wirsansky has introduced two captivating chapters: “Natural Language Processing (NLP)”, and “Explainable AI, Causality, and Counterfactuals with Genetic Algorithms”. These additions are not only timely but also extremely impactful, given the current prominence of these topics. The discussion on counterfactuals, though concise, manages to be both informative and profound, providing readers with a nuanced understanding of its applications. I eagerly anticipate the third edition, hoping to see more examples of GAs applied to XAI and Causality.A notable addition to this edition is the chapter on Enhancing Performance with Concurrency and Cloud Strategies. This is particularly relevant for professionals dealing with big data or projects that demand swift execution. It introduces a new dimension to the book, equipping readers with strategies to handle computational challenges efficiently.The final chapter offers a glimpse into other evolutionary and bio-inspired computation methods, serving as a valuable guide for fundamental researchers and curious learners looking to expand their knowledge beyond Genetic Algorithms. This "where-to-go" section opens new avenues for exploration and study.Looking forward, it would be beneficial for future editions to address the (current) limitations of GAs. Given Eyal Wirsansky's expertise in both GAs and Deep Learning (DL), an exploration of how GAs could potentially revolutionize DL in the future would be particularly fascinating and insightful.

We are pleased to share a comprehensive review of "GitHub for Next-Generation Coders", published by Packt, and written by Sorin Pasa. 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:"GitHub for Next-Generation Coders" by Igor Irić is a meticulously crafted guide that seamlessly bridges the gap between novice and proficient GitHub users, primarily targeting young coders eager to harness the full potential of this indispensable platform. Given Igor's extensive background as a senior Azure solutions architect and GitHub trainer, the book benefits from his profound expertise and practical experience. This is evident throughout the text, which is rich with real-world applications and actionable insights.  What did you find most valuable in the book you've reviewed?  One of the most valuable aspects of the book is its structured approach to teaching GitHub. The content is methodically divided into clear, progressive sections that cater to different levels of expertise. Starting with an introduction to version control and GitHub basics, Igor ensures that even readers with minimal background can follow along comfortably. The sections on collaborative development using GitHub and mastering Git commands are particularly beneficial, offering practical advice on handling real-world scenarios like resolving merge conflicts and making effective pull requests. The emphasis on GitHub Actions for automation and enhancing security measures is also timely and relevant, given the increasing focus on DevOps and secure coding practices.  How does the book compare to competitors in the field?  Compared to other books in the same domain, "GitHub for Next-Generation Coders" stands out for its comprehensive yet accessible approach. Many competing titles either delve too deeply into advanced topics too quickly or skim over essential basics, leaving beginners overwhelmed or inadequately prepared. Igor's book strikes a fine balance, providing a solid foundation before moving on to more complex functionalities. Furthermore, the inclusion of GitHub Copilot and its role in aiding code creation is a unique addition, reflecting the latest advancements in AI-driven development tools.  Who would benefit the most from the book?  This book is ideally suited for young coders, students, and early-career developers who are new to GitHub and version control systems. However, even seasoned developers will find value in the advanced sections, particularly those focused on GitHub Actions and security. Educators and mentors looking for a reliable resource to introduce GitHub to their students will also find this book immensely useful.  Can you share a personal anecdote related to the content?  As a developer who has mentored junior team members, I often encounter the challenge of explaining the nuances of version control and collaborative coding. I recall an instance where a new team member struggled with resolving a merge conflict. The clear, step-by-step guidance provided in Igor's book on this topic mirrors the advice I had to piece together from various sources. Having such a resource would have made the process smoother and less intimidating for the new coder.  What unique insights or perspectives does the book offer?  Igor Irić brings a unique mentor's perspective to the book, which is evident in the way he anticipates common pitfalls and queries that new users might have. His extensive experience with global enterprises and cutting-edge technology strategies informs the book's content, particularly in areas like security and automation. The focus on engaging with the open-source community is another standout feature, encouraging readers to not only use GitHub as a tool but also to contribute and collaborate on a broader scale. This holistic view of GitHub as both a development and a community platform sets the book apart.  In conclusion, "GitHub for Next-Generation Coders" is an invaluable resource that promises to equip young coders with the skills and confidence needed to excel in collaborative software development. Igor Irić's expertise and passion for knowledge-sharing shine through, making this book a must-have for anyone looking to master GitHub. Reviewer BioSorin Pasa is a seasoned cloud architect and Microsoft Certified Trainer with nearly 20 years of experience in the IT industry. Well known for his expertise in designing and implementing scalable cloud solutions, Sorin has consistently driven innovation and efficiency across various organizations and industries, in multiple countries. His deep knowledge of Microsoft technologies, combined with a passion for teaching, has empowered countless professionals to enhance their skills. Sorin’s commitment to excellence and his ability to navigate complex technical landscapes make him a pivotal figure in the cloud computing community.

We are pleased to share a comprehensive review of "Effective .NET Memory Management", published by Packt, and written by Johnnie Jones. 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 a Senior Machine Learning Engineer, optimizing applications for performance is at the core of what I do, especially when dealing with large datasets and resource-intensive computations. Memory management, often underestimated, plays a pivotal role in how well an application performs and scales. That’s why Trevoir Williams’ Effective .NET Memory Management immediately resonated with me as a must-read for anyone working within the .NET ecosystem.This book expertly navigates the intricacies of memory management in .NET Core, from the fundamentals of memory allocation to advanced garbage collection techniques. What sets Trevoir’s work apart is his ability to bridge theory with practical, real-world application. His hands-on examples and clear explanations make even complex topics like multithreading and asynchronous programming both accessible and highly relevant.One of the standout aspects of the book is its focus on memory profiling and optimization—areas that are critical in fields like machine learning, where performance bottlenecks can have significant implications. Trevoir provides actionable insights on minimizing memory fragmentation, managing object lifetimes, and effectively utilizing stack and heap memory. These are essential skills for any developer aiming to write efficient, high-performance code.Trevoir’s structured approach, starting with core principles and advancing to more complex techniques, ensures that this book is valuable whether you’re an experienced developer or someone looking to deepen your understanding of .NET Core’s memory management. The sections on troubleshooting memory leaks and diagnosing performance issues are particularly practical, addressing challenges that every developer encounters sooner or later.In a world where software efficiency and scalability are crucial, Effective .NET Memory Management arms developers with the tools and knowledge to optimize their applications. Trevoir’s insights will help you not just understand how memory is managed but also how to leverage that understanding to build faster, more reliable software.In conclusion: This book is a vital resource for any .NET developer serious about mastering memory management. Trevoir Williams delivers a thorough and practical guide that deserves a place in every developer’s library. 

We are pleased to share a comprehensive review of "Systems Programming with C# and .NET", published by Packt, and written by Ankit Srivastava. This review offers an in-depth exploration of the book's key themes and insights, providing readers with a thorough understanding of its value. Unlock the full potential of C# and .NET Core in systems programming to secure, deploy, and maintain robust applications. With this book, you’ll focus on low-level APIs, memory management, and performance optimization.Please find the review below:What did you find most valuable in the book you've reviewed? The content is amazing i.e., It covers the very basics to advanced system programming. Author has tried multiple ways to interact with people by providing code snippets, assembly  conversion, decoding it, and getting a better understanding. A lot of books only focus on one area and don't teach us about the other things. This is one of the all rounder books for systems programming where we covered multiple areas e.g., Logging, Security, Performance, etc. How does the book compare to competitors in the field? Not many books are written in this area i.e., which focuses on system programming with C#. In the past people actually wrote books on kernel programming but mainly it was in either C or C++ and hence, this book became one of the out of league books which offer such learning in a .NET environment. Who would benefit the most from the book? A young college student to an experienced developer. All would learn something from this book. The content is extraordinary and the author has done an excellent job in articulating everything. Can you share a personal anecdote related to the content?   When I first got the chance to review a book on C# systems programming, I was genuinely excited. I’d been working on complex .NET projects for years, but diving into the systems-level side of things wasn’t something I’d really explored. As I started reading, what really impressed me was how the book made systems programming feel accessible. It seamlessly connected what I already knew about high-level application development with the more intricate, behind-the-scenes system interactions.   What unique insights or perspectives does the book offer? C# learning is offered by other books as well. Here the author considers that someone is already having hands on to C# and the unique things are Systems programming, Multithreading, Logging, Security, Deployment strategies, CI/CD, Risk mitigation, effective troubleshooting, profiling tools, etc. makes it one of the very few books. Reviewer BioAnkit Srivastava is a seasoned Senior Developer at Walmart; boasting seven years of extensive experience in software development. He specializes in .NET Development, Windows Development, WPF, WCF, REST API, .NET Core, .NET Standard, Python, and Linux. Ankit earned his Bachelor of Technology degree in Information Technology from Harcourt Butler Technological Institute and holds certifications in C#, C/C++, Python, Linux, Java, and SQL. Throughout his career, he has made signifi cant contributions to diverse domains, including Semiconductors, Automotive, Storage, Chemical Heat Exchangers, and Health and Wellness.

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​.

We are pleased to share a comprehensive review of "Zabbix 7 IT Infrastructure Monitoring Cookbook", published by Packt, and written by Andreas Drbal. 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:"Zabbix 7 IT Infrastructure Monitoring Cookbook" is a concise and practical guide that dives into a tool, Zabbix, I both personally and professionally enjoy using. The book is well-written and provides hands-on examples for applying monitoring to applications, making it a valuable resource for CloudOps, DevOps, or Site Reliability Engineers.What did you find most valuable in the book you've reviewed?The immersive and hands-on sections were particularly valuable which reinforced concepts of monitoring and alerting for an application or network engineer. The practical examples, such as implementing Docker container monitoring using Zabbix Agent 2, made complex tasks approachable and easy to execute.How does the book compare to competitors in the field?This book stands out for its simplicity and well-structured content that's easy to digest. Unlike other resources, it allows readers to jump to various chapters independently, providing flexibility and catering to different learning needs.Who would benefit the most from the book?Engineers and professionals involved in infrastructure monitoring—especially those in CloudOps, DevOps, or Site Reliability roles—would benefit greatly from this book. It's also useful for anyone looking to implement a powerful monitoring solution in any environment that they manage that is connected to the network.Can you share a personal anecdote related to the content?Recently, my team implemented Docker container monitoring using Zabbix Agent 2, and the steps outlined in the book made the process easy to follow and implement. It was like having a seasoned expert guiding us!What unique insights or perspectives does the book offer?The authors, Nathan Liefting and Brian van Baekel, draw upon their professional and personal experience with Zabbix and add their personal industry insights to highlight effective ways to monitor networks and applications. Particularly their experience with the Simple Network Management Protocol (SNMP) was enjoyable to test and implement in our monitoring stack.In conclusion, "Zabbix 7 IT Infrastructure Monitoring Cookbook" is a practical and valuable resource that equips engineers with the skills needed to effectively monitor IT infrastructure. Its hands-on approach and the authors' expertise make it a must-have for professionals seeking to enhance their monitoring capabilities.Reviewer BioAndreas Drbal boasts a decade in tech, evolving from a self-taught frontend developer to a key fi gure in DevOps and CloudOps. His academic journey led him to UCLA, majoring in English with a German minor, and later acquiring a master’s in information systems management and an MBA. Andreas’s passion for monitoring began with Zabbix 3.x in 2015, integrating it with IoT devices. Currently, as a senior manager at Cloud Soft ware Group, he’s driven by automation, streamlining the DevOps life cycle, and ensuring team productivity.

We are pleased to share a comprehensive review of Python for Algorithmic Trading Cookbook, published by Packt Publishing, and written by Brian Schneider. 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:1.  The most valuable aspect of the book is its comprehensive aggregation of financial theory and Python to enable the reader with skills and tools to implement algorithmic trading systems.  The book saves the reader countless hours of research and coding by providing the latest tools, financial theory, and coding steps all in one place.  This gives the reader a tremendous head start on their algorithmic trading journey.   2.  The book is unique in that it includes cutting-edge Python modules to provide readers with instant results, as opposed to delving too deep into theory and code.  Many books in this area, generally do not provide this well-balanced approach, including detailed instructions for the reader to implement their own strategies with broker APIs.  The book streamlines the trading strategy evaluation process, arming the reader with valuable skills and tools to identify their best ideas quickly; while providing a solid balance of both quantitative finance theory and Python. 3.  This book is a true 'how-to' for algorithmic trading practitioners and enthusiasts alike.  The book provides seasoned or novice traders the needed steps to go from trading ideas, to code implementation - with no Pythonic syntax left out!  I wish I had a book like this when I started. 4.  I was fortunate to have worked with Jason on automated investment strategies in the past and his insight and clarity are unmatched.  The algorithmic strategies we developed overlap with topics in the book, however, Jason has splendidly pulled together the complete end-to-end recipe!   Equally impressive, is the amount of research Jason has put into finding the latest tools in the Python universe to save the reader time.  The book was a pleasure to read  5.  I am most impressed with the book's comprehensive coverage of the subject matter.  The book details critical steps needed to enable an algorithmic trading strategy, without the reader losing sleep at night.   The book doesn't dive too deep into theory and math with many readers having that background, conversely, it provides readers with theoretical justifications and how to implement them efficiently. Reviewer BioBrian Schneider holds a Bachelor of Science degree in electrical engineering and a Master of Science in financial engineering. He has 25 years of experience in systems engineering, software design and architecture, algorithm performance, data science, and analytics. He has a passion for financial markets and automating algorithmic trading systems and co-founded an investment management company in that space.