Artificial Intelligence With Agile
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Artificial Intelligence with Agile
"Artificial Intelligence with Agile: Integrating AI in Your Projects" is a comprehensive guide that bridges the gap between AI technologies and Agile methodologies. It is designed to equip professionals with the knowledge and skills needed to effectively integrate AI into Agile projects. The book offers an in-depth exploration of AI's core concepts, applications across various industries, and the principles of Agile project management. By combining detailed theoretical insights with practical examples and case studies, the text provides readers with a well-rounded understanding of how these two powerful paradigms can be synchronized to enhance project outcomes. This book covers a broad spectrum of topics, from planning and requirement gathering for AI projects to the intricacies of building AI-powered systems. It delves into essential areas such as data management, continuous integration and deployment, and the testing and validation of AI systems. Additionally, it addresses critical ethical and governance aspects and offers strategies for scaling and optimizing AI solutions. By the end of this book, readers will have a clear roadmap for leveraging AI's capabilities within the Agile framework, ensuring successful and efficient project execution.
Agile Artificial Intelligence in Pharo
Cover classical algorithms commonly used as artificial intelligence techniques and program agile artificial intelligence applications using Pharo. This book takes a practical approach by presenting the implementation details to illustrate the numerous concepts it explains. Along the way, you’ll learn neural net fundamentals to set you up for practical examples such as the traveling salesman problem and cover genetic algorithms including a fun zoomorphic creature example. Furthermore, Practical Agile AI with Pharo finishes with a data classification application and two game applications including a Pong-like game and a Flappy Bird-like game. This book is informative and fun, giving you source code to play along with. You’ll be able to take this source code and apply it to your own projects. What You Will Learn Use neurons, neural networks, learning theory, and more Work with genetic algorithms Incorporate neural network principles when working towards neuroevolution Include neural network fundamentals when building three Pharo-based applications Who This Book Is For Coders and data scientists who are experienced programmers and have at least some prior experience with AI or deep learning. They may be new to Pharo programming, but some prior experience with it would be helpful.
Artificial Intelligence
The first text to take a systems engineering approach to artificial intelligence (AI), from architecture principles to the development and deployment of AI capabilities. Most books on artificial intelligence (AI) focus on a single functional building block, such as machine learning or human-machine teaming. Artificial Intelligence takes a more holistic approach, addressing AI from the view of systems engineering. The book centers on the people-process-technology triad that is critical to successful development of AI products and services. Development starts with an AI design, based on the AI system architecture, and culminates with successful deployment of the AI capabilities. Directed toward AI developers and operational users, this accessibly written volume of the MIT Lincoln Laboratory Series can also serve as a text for undergraduate seniors and graduate-level students and as a reference book. Key features: In-depth look at modern computing technologies Systems engineering description and means to successfully undertake an AI product or service development through deployment Existing methods for applying machine learning operations (MLOps) AI system architecture including a description of each of the AI pipeline building blocks Challenges and approaches to attend to responsible AI in practice Tools to develop a strategic roadmap and techniques to foster an innovative team environment Multiple use cases that stem from the authors’ MIT classes, as well as from AI practitioners, AI project managers, early-career AI team leaders, technical executives, and entrepreneurs Exercises and Jupyter notebook examples