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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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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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How today’s enterprise architect juggles strategy, tech and innovation

CIO

Observer-optimiser: Continuous monitoring, review and refinement is essential. enterprise architects ensure systems are performing at their best, with mechanisms (e.g. Ecosystem warrior: Enterprise architects manage the larger ecosystem, addressing challenges like sustainability, vendor management, compliance and risk mitigation.

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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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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.

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CIOs are rethinking how they use public cloud services. Here’s why.

CIO

Increasingly, however, CIOs are reviewing and rationalizing those investments. The reasons include higher than expected costs, but also performance and latency issues; security, data privacy, and compliance concerns; and regional digital sovereignty regulations that affect where data can be located, transported, and processed.

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