Tags
Language
Tags
November 2025
Su Mo Tu We Th Fr Sa
26 27 28 29 30 31 1
2 3 4 5 6 7 8
9 10 11 12 13 14 15
16 17 18 19 20 21 22
23 24 25 26 27 28 29
30 1 2 3 4 5 6
    Attention❗ To save your time, in order to download anything on this site, you must be registered 👉 HERE. If you do not have a registration yet, it is better to do it right away. ✌

    ( • )( • ) ( ͡⚆ ͜ʖ ͡⚆ ) (‿ˠ‿)
    SpicyMags.xyz

    Artificial Intelligence: A Systems Approach from Architecture Principles to Deployment

    Posted By: readerXXI
    Artificial Intelligence: A Systems Approach from Architecture Principles to Deployment

    Artificial Intelligence: A Systems Approach from Architecture Principles to Deployment
    by David R. Martinez and Bruke M. Kifle
    English | 2024 | ISBN: 0262048981 | 577 Pages | PDF | 64 MB

    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