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    Platform and Model Design for Responsible AI. Design and build resilient, private, fair, and transparent machine learning models

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Amita Kapoor, Sharmistha Chatterjee
    Platform and Model Design for Responsible AI. Design and build resilient, private, fair, and transparent machine learning models Amita Kapoor, Sharmistha Chatterjee - okladka książki

    Platform and Model Design for Responsible AI. Design and build resilient, private, fair, and transparent machine learning models Amita Kapoor, Sharmistha Chatterjee - okladka książki

    Platform and Model Design for Responsible AI. Design and build resilient, private, fair, and transparent machine learning models Amita Kapoor, Sharmistha Chatterjee - audiobook MP3

    Platform and Model Design for Responsible AI. Design and build resilient, private, fair, and transparent machine learning models Amita Kapoor, Sharmistha Chatterjee - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    516
    Dostępne formaty:
         PDF
         ePub
    AI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it’s necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you’ll be able to make existing black box models transparent.
    You’ll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You’ll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you’ll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You’ll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics.
    By the end of this book, you’ll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You’ll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions.

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    O autorach książki

    Amita Kapoor od dwudziestu lat wykłada wiedzę o sieciach neuronowych na Uniwersytecie w Delhi. Interesuje się uczeniem maszynowym, sieciami neuronowymi, robotyką oraz buddyzmem i etyką w sztucznej inteligencji.

    Sharmistha Chatterjee is an evangelist in the field of machine learning (ML) and cloud applications, currently working in the BFSI industry at the Commonwealth Bank of Australia in the data and analytics space. She has worked in Fortune 500 companies, as well as in early-stage start-ups. She became an advocate for responsible AI during her tenure at Publicis Sapient, where she led the digital transformation of clients across industry verticals. She is an international speaker at various tech conferences and a 2X Google Developer Expert in ML and Google Cloud. She has won multiple awards and has been listed in 40 under 40 data scientists by Analytics India Magazine (AIM) and 21 tech trailblazers in 2021 by Google. She has been involved in responsible AI initiatives led by Nasscom and as part of their DeepTech Club.

    Amita Kapoor, Sharmistha Chatterjee - pozostałe książki

    Packt Publishing - inne książki

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