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The key to operational AI: Modern data architecture

CIO

However, the biggest challenge for most organizations in adopting Operational AI is outdated or inadequate data infrastructure. To succeed, Operational AI requires a modern data architecture. Ensuring effective and secure AI implementations demands continuous adaptation and investment in robust, scalable data infrastructures.

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Can serverless fix fintech’s scaling problem?

CIO

Add to this the escalating costs of maintaining legacy systems, which often act as bottlenecks for scalability. The latter option had emerged as a compelling solution, offering the promise of enhanced agility, reduced operational costs, and seamless scalability. Scalability. Legacy infrastructure. Architecture complexity.

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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO

The next phase of this transformation requires an intelligent data infrastructure that can bring AI closer to enterprise data. As the next generation of AI training and fine-tuning workloads takes shape, limits to existing infrastructure will risk slowing innovation.

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Automate Amazon Bedrock batch inference: Building a scalable and efficient pipeline

AWS Machine Learning - AI

To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. Solution overview The solution presented in this post uses batch inference in Amazon Bedrock to process many requests efficiently using the following solution architecture.

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Choosing a cloud infrastructure provider: A beginner’s guide

TechCrunch

Deploying cloud infrastructure also involves analyzing tools and software solutions, like application monitoring and activity logging, leading many developers to suffer from analysis paralysis. These companies are worried about the future of their cloud infrastructure in terms of security, scalability and maintainability.

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CIOs contend with gen AI growing pains

CIO

Unfortunately, despite hard-earned lessons around what works and what doesn’t, pressure-tested reference architectures for gen AI — what IT executives want most — remain few and far between, she said. It’s time for them to actually relook at their existing enterprise architecture for data and AI,” Guan said. “A

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Reimagine application modernisation with the power of generative AI

CIO

According to research from NTT DATA , 90% of organisations acknowledge that outdated infrastructure severely curtails their capacity to integrate cutting-edge technologies, including GenAI, negatively impacts their business agility, and limits their ability to innovate. [1]