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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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Personalize your generative AI applications with Amazon SageMaker Feature Store

AWS Machine Learning

This movie is a masterclass in storytelling, with a deep sense of history and culture that will transport you to Middle-earth and leave you wanting more. He supports strategic customers with AI/ML best practices cross many industries. Outside of work, he enjoys reading and traveling.

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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

About the Authors Charalampos Grouzakis is a Data Scientist within AWS Professional Services. He has over 11 years of experience in developing and leading data science, machine learning, and big data initiatives. Her skills and areas of expertise include data science, machine learning, and big data.

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The IoT Chronicles Part 2: Three Big Security Threats—and How to Solve Them

Avaya

In fact, research shows that about 90% of all data in the world today was created in just the past few years (2.5 billion GB of data were being produced every day in 2012 alone!) Ultimately, end-to-end segmentation transforms your network core into an automated and intelligent virtualized transport.

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Demystifying machine learning at the edge through real use cases

AWS Machine Learning

Edge is a term that refers to a location, far from the cloud or a big data center, where you have a computer device (edge device) capable of running (edge) applications. Agriculture, mining, surveillance and security, and maritime transportation are some areas where far edge devices play an important role. Edge computing.

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Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

AWS Machine Learning

Amazon Cognito for user authentication with Transport Layer Security (TLS). Data must be utilized, stored, and accessed in specific ways, and we have embedded robust processes to ensure our practices comply with our legal obligations as well as align with industry best practices.