Description
Architecting AI Software Systems provides a definitive guide to building AI-enabled systems, emphasizing the balance between AI’s capabilities and traditional software architecture principles.
As AI technologies gain widespread acceptance and are increasingly expected in future applications, this book provides architects and developers with the essential knowledge to stay competitive. It introduces a structured approach to mastering the complexities of AI integration, covering key architectural concepts and processes critical to building scalable and robust AI systems while minimizing development and maintenance risks. The book guides readers on a progressive journey, using real-world examples and hands-on exercises to deepen comprehension. It also includes the architecture of a fictional AI-enabled system as a learning tool. You will engage with exercises designed to reinforce your understanding and apply practical insights, leading to the development of key architectural products that support AI systems. This is an essential resource for architects seeking to mitigate risks and master the complexities of AI-enabled system development.
By the end of the book, readers will be equipped with patterns, strategies and concepts necessary to architect AI-enabled systems across various domains.
What You Will Learn:
– Understand the challenges of building AI-enabled systems and managing risks like underperformance and cost overruns
– Learn architectural tools to design and integrate AI into traditional systems
– Master AI/ML concepts like inference and decision-making and their impact on architecture
– Use architectural models to ensure system cohesion and functionality
– Simulate and optimize AI performance through prototyping and iteration
– Design scalable AI systems using patterns and heuristics
– Integrate AI into large systems with a focus on user experience and performance
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