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    Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats Srinivasa Rao Aravilli, Sam Hamilton

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats Srinivasa Rao Aravilli, Sam Hamilton - okladka książki

    Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats Srinivasa Rao Aravilli, Sam Hamilton - okladka książki

    Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats Srinivasa Rao Aravilli, Sam Hamilton - audiobook MP3

    Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats Srinivasa Rao Aravilli, Sam Hamilton - audiobook CD

    Autorzy:
    Srinivasa Rao Aravilli, Sam Hamilton
    Serie wydawnicze:
    Learning
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    402
    Dostępne formaty:
         PDF
         ePub
    – In an era of evolving privacy regulations, compliance is mandatory for every enterprise

    – Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information

    – This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases

    – As you progress, you’ll be guided through developing anti-money laundering solutions using federated learning and differential privacy

    – Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models

    – You’ll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field

    – Upon completion, you’ll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks

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    O autorze książki

    Srinivasa Rao Aravilli boasts 27 years of extensive experience in technology, research, and leadership roles, spearheading innovation in various domains such as Information Retrieval, Search, ML/AI, Distributed Computing, Network Analytics, Privacy, and Security. Currently working as a Senior Director of Machine Learning Engineering at Capital One, Bangalore, he has a proven track record of driving new products from conception to outstanding customer success. Prior to his tenure at Capital One, Srinivasa held prominent leadership positions at Visa, Cisco, and Hewlett Packard, where he led product groups focused on data privacy, machine learning, and Generative AI. He holds a Master's Degree in Computer Applications from Andhra University, Visakhapatnam, India.

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