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    Machine Learning Security with Azure. Best practices for assessing, securing, and monitoring Azure Machine Learning workloads Georgia Kalyva, George Kavvalakis

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Machine Learning Security with Azure. Best practices for assessing, securing, and monitoring Azure Machine Learning workloads Georgia Kalyva, George Kavvalakis - okladka książki

    Machine Learning Security with Azure. Best practices for assessing, securing, and monitoring Azure Machine Learning workloads Georgia Kalyva, George Kavvalakis - okladka książki

    Machine Learning Security with Azure. Best practices for assessing, securing, and monitoring Azure Machine Learning workloads Georgia Kalyva, George Kavvalakis - audiobook MP3

    Machine Learning Security with Azure. Best practices for assessing, securing, and monitoring Azure Machine Learning workloads Georgia Kalyva, George Kavvalakis - audiobook CD

    Autorzy:
    Georgia Kalyva, George Kavvalakis
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    310
    Dostępne formaty:
         PDF
         ePub
    With AI and machine learning (ML) models gaining popularity and integrating into more and more applications, it is more important than ever to ensure that models perform accurately and are not vulnerable to cyberattacks. However, attacks can target your data or environment as well. This book will help you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to applications and infrastructure.
    This book begins by introducing what some common ML attacks are, how to identify your risks, and the industry standards and responsible AI principles you need to follow to gain an understanding of what you need to protect. Next, you will learn about the best practices to secure your assets. Starting with data protection and governance and then moving on to protect your infrastructure, you will gain insights into managing and securing your Azure ML workspace. This book introduces DevOps practices to automate your tasks securely and explains how to recover from ML attacks. Finally, you will learn how to set a security benchmark for your scenario and best practices to maintain and monitor your security posture.
    By the end of this book, you’ll be able to implement best practices to assess and secure your ML assets throughout the Azure Machine Learning life cycle.

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    O autorze książki

    Georgia Kalyva is a technical trainer at Microsoft. She was recognized as a Microsoft AI MVP, is a Microsoft Certified Trainer, and is an international speaker with more than 10 years of experience in Microsoft Cloud, AI, and developer technologies. Her career covers several areas, ranging from designing and implementing solutions to business and digital transformation. She holds a bachelor's degree in informatics from the University of Piraeus, a master's degree in business administration from the University of Derby, and multiple Microsoft certifications. Georgia's honors include several awards from international technology and business competitions, and her journey to excellence stems from a growth mindset and a passion for technology.

    Packt Publishing - inne książki

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