Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Ai Engineering: Building Reliable Intelligent Systems

Ai Engineering: Building Reliable Intelligent Systems

Paperback

General Computers

Currently unavailable to order

ISBN13: 9798170570300
Publisher: Independently Published
Pages: 40
Weight: 0.26
Height: 0.08 Width: 8.50 Depth: 11.00
Language: English
Ai Engineering: Building Reliable Intelligent Systems tackles the critical challenge of moving artificial intelligence from promising research concepts to robust, trustworthy, and impactful real-world applications. In an era where AI promises transformative potential, many projects falter not due to algorithmic complexity, but fundamental flaws in their underlying infrastructure and approach. This book provides a comprehensive engineering blueprint for practitioners, data scientists, and leaders committed to delivering AI solutions that consistently perform.

At the heart of any successful AI deployment lies a meticulously crafted data foundation. This book confronts the pervasive Garbage In, Garbage Out (GIGO) problem, revealing how flawed, incomplete, or biased data is the single most common reason AI projects fail in production environments. It moves beyond theoretical discussions to present a disciplined, systematic approach to data as a core engineering discipline. You will discover practical frameworks for intentional data acquisition, including defining clear requirements, identifying reliable sources, and navigating critical governance and ethical considerations. The book then delves into the often-underestimated 80% of AI work: rigorous data cleaning-addressing missing values, outlier detection, and ensuring consistency. Finally, it guides you through data transformation, from feature engineering to scaling and encoding, ensuring your data is perfectly shaped for optimal model consumption.

Beyond data, Ai Engineering extends its focus to the broader ecosystem necessary for real-world success. It bridges the gap between academic research and practical deployment, offering strategies for integrating AI models into existing systems and ensuring their operational stability. The book also addresses the paramount importance of Responsible AI, providing insights into navigating ethical dilemmas, mitigating bias, and understanding the societal impact of your intelligent systems. You will learn how to orchestrate multidisciplinary AI teams, fostering collaboration between data scientists, engineers, domain experts, and ethicists. Furthermore, it emphasizes iteration and adaptation as the engine of AI evolution, advocating for continuous improvement and robust MLOps practices.

By adopting the principles and methodologies outlined in Ai Engineering, you will gain the expertise to:

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