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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

As systems scale, conducting thorough AWS Well-Architected Framework Reviews (WAFRs) becomes even more crucial, offering deeper insights and strategic value to help organizations optimize their growing cloud environments. In this post, we explore a generative AI solution leveraging Amazon Bedrock to streamline the WAFR process.

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AI & the enterprise: protect your data, protect your enterprise value

CIO

Maintaining a clear audit trail is essential when data flows through multiple systems, is processed by various groups, and undergoes numerous transformations. This is an important element in regulatory compliance and data quality. AI companies and machine learning models can help detect data patterns and protect data sets.

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How to know a business process is ripe for agentic AI

CIO

Does [it] have in place thecompliance review and monitoring structure to initially evaluate the risks of the specific agentic AI; monitor and correct where issues arise; measure success; remain up to date on applicable law and regulation? The agent acts as a bridge across teams to ensure smoother workflows and decision-making, she says.

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Dulling the impact of AI-fueled cyber threats with AI

CIO

These days, digital spoofing, phishing attacks, and social engineering attempts are more convincing than ever due to bad actors refining their techniques and developing more sophisticated threats with AI. Moreover, this can cause companies to fall short of regulatory compliance, with these data potentially being misused.

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Why you should care about debugging machine learning models

O'Reilly Media - Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems.

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Unlocking the full potential of enterprise AI

CIO

Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] Without the necessary guardrails and governance, AI can be harmful.

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What is data architecture? A framework to manage data

CIO

Data architecture goals The goal of data architecture is to translate business needs into data and system requirements, and to manage data and its flow through the enterprise. AI and machine learning models. AI and ML are used to automate systems for tasks such as data collection and labeling. Container orchestration.