Remove Enterprise Remove Generative AI Remove Machine Learning
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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] 4] On their own AI and GenAI can deliver value.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

While organizations continue to discover the powerful applications of generative AI , adoption is often slowed down by team silos and bespoke workflows. To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle.

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Have we reached the end of ‘too expensive’ for enterprise software?

CIO

The commodity effect of LLMs over specialized ML models One of the most notable transformations generative AI has brought to IT is the democratization of AI capabilities. Companies can enrich these versatile tools with their own data using the RAG (retrieval-augmented generation) architecture.

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Generative AI operating models in enterprise organizations with Amazon Bedrock

AWS Machine Learning - AI

Generative AI can revolutionize organizations by enabling the creation of innovative applications that offer enhanced customer and employee experiences. In this post, we evaluate different generative AI operating model architectures that could be adopted.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

In this post, we explore a generative AI solution leveraging Amazon Bedrock to streamline the WAFR process. We demonstrate how to harness the power of LLMs to build an intelligent, scalable system that analyzes architecture documents and generates insightful recommendations based on AWS Well-Architected best practices.

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Track, allocate, and manage your generative AI cost and usage with Amazon Bedrock

AWS Machine Learning - AI

As enterprises increasingly embrace generative AI , they face challenges in managing the associated costs. With demand for generative AI applications surging across projects and multiple lines of business, accurately allocating and tracking spend becomes more complex.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

Organizations are increasingly using multiple large language models (LLMs) when building generative AI applications. An example is a virtual assistant for enterprise business operations. He specializes in machine learning and is a generative AI lead for NAMER startups team.