AI

Beyond pilots: A proven framework for scaling AI to production

The era of perpetual AI pilots is over. This year, 65% of AWS Generative AI Innovation Center customer projects moved from concept to production—some launching in just 45 days, as AWS VP Swami Sivasubramanian shared on LinkedIn. These results come from insights gained across more than one thousand customer implementations. The Generative AI Innovation Center […]

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Generate Gremlin queries using Amazon Bedrock models

Graph databases have revolutionized how organizations manage complex, interconnected data. However, specialized query languages such as Gremlin often create a barrier for teams looking to extract insights efficiently. Unlike traditional relational databases with well-defined schemas, graph databases lack a centralized schema, requiring deep technical expertise for effective querying. To address this challenge, we explore an

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Incorporating responsible AI into generative AI project prioritization

Over the past two years, companies have seen an increasing need to develop a project prioritization methodology for generative AI. There is no shortage of generative AI use cases to consider. Rather, companies want to evaluate the business value against the cost, level of effort, and other concerns, for a large number of potential generative

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Build scalable creative solutions for product teams with Amazon Bedrock

Creative teams and product developers are constantly seeking ways to streamline their workflows and reduce time to market while maintaining quality and brand consistency. This post demonstrates how to use AWS services, particularly Amazon Bedrock, to transform your creative processes through generative AI. You can implement a secure, scalable solution that accelerates your creative workflow,

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Build a proactive AI cost management system for Amazon Bedrock – Part 2

In Part 1 of our series, we introduced a proactive cost management solution for Amazon Bedrock, featuring a robust cost sentry mechanism designed to enforce real-time token usage limits. We explored the core architecture, token tracking strategies, and initial budget enforcement techniques that help organizations control their generative AI expenses. Building upon that foundation, this

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