Favorite Today, organizations are constantly seeking ways to use advanced large language models (LLMs) for their specific needs. These organizations are engaging in both pre-training and fine-tuning massive LLMs, with parameter counts in the billions. This process aims to enhance model efficacy for a wide array of applications across diverse

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Shared by AWS Machine Learning December 6, 2024

Favorite Generative AI has empowered customers with their own information in unprecedented ways, reshaping interactions across various industries by enabling intuitive and personalized experiences. This transformation is significantly enhanced by Retrieval Augmented Generation (RAG), which is a generative AI pattern where the large language model (LLM) being used references a

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Shared by AWS Machine Learning December 6, 2024

Favorite As generative AI continues to drive innovation across industries and our daily lives, the need for responsible AI has become increasingly important. At AWS, we believe the long-term success of AI depends on the ability to inspire trust among users, customers, and society. This belief is at the heart

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Shared by AWS Machine Learning December 6, 2024

Favorite Organizations of every size and across every industry are looking to use generative AI to fundamentally transform the business landscape with reimagined customer experiences, increased employee productivity, new levels of creativity, and optimized business processes. A recent study by Telecom Advisory Services, a globally recognized research and consulting firm that

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Shared by AWS Machine Learning December 5, 2024

Favorite Amazon Kendra is an intelligent enterprise search service that helps you search across different content repositories with built-in connectors. AWS customers use Amazon Kendra with large language models (LLMs) to quickly create secure, generative AI–powered conversational experiences on top of your enterprise content. As enterprises adopt generative AI, many

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Shared by AWS Machine Learning December 5, 2024

Favorite Scaling machine learning (ML) workflows from initial prototypes to large-scale production deployment can be daunting task, but the integration of Amazon SageMaker Studio and Amazon SageMaker HyperPod offers a streamlined solution to this challenge. As teams progress from proof of concept to production-ready models, they often struggle with efficiently

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Shared by AWS Machine Learning December 5, 2024

Favorite Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. To address these challenges, we introduce Amazon Bedrock IDE, an integrated environment for developing and customizing generative AI applications. Formerly known as Amazon Bedrock Studio, Amazon

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Shared by AWS Machine Learning December 5, 2024

Favorite When using generative AI, achieving high performance with low latency models that are cost-efficient is often a challenge, because these goals can clash with each other. With the newly launched Amazon Bedrock Model Distillation feature, you can use smaller, faster, and cost-efficient models that deliver use-case specific accuracy that

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Shared by AWS Machine Learning December 5, 2024

Favorite Today, we’re excited to announce a new capability that allows you to deploy over 100 open-weight and proprietary models from Amazon SageMaker JumpStart and register them with Amazon Bedrock, allowing you to seamlessly access them through the powerful Amazon Bedrock APIs. You can now use Amazon Bedrock features such

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Shared by AWS Machine Learning December 5, 2024

Favorite This post is cowritten with Isaac Cameron and Alex Gnibus from Tecton. Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI

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Shared by AWS Machine Learning December 5, 2024