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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 knowledge corpus outside of its training …

Deploy RAG applications on Amazon SageMaker JumpStart using FAISS Read More »

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 sectors, including healthcare, financial services, and …

Speed up your cluster procurement time with Amazon SageMaker HyperPod training plans Read More »

Learn more about the latest Pixel and Android updates, featuring the latest in Google AI innovation.

In today’s rapidly evolving digital landscape, software companies are at the forefront of innovation. These organizations are uniquely positioned to take advantage of AI-powered solutions to drive growth, enhance customer experiences, and stay ahead of the competition. AI is opening the door to valuable opportunities for software companies by accelerating cloud migration, app modernization, and …

Unveiling the future of AI innovation for ISVs Read More »

This post is co-written with Abhishek Sawarkar, Eliuth Triana, Jiahong Liu and Kshitiz Gupta from NVIDIA.  At AWS re:Invent 2024, we are excited to introduce Amazon Bedrock Marketplace. This a revolutionary new capability within Amazon Bedrock that serves as a centralized hub for discovering, testing, and implementing foundation models (FMs). It provides developers and organizations …

Amazon Bedrock Marketplace now includes NVIDIA models: Introducing NVIDIA Nemotron-4 NIM microservices Read More »

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 use cases make it to production. …

Real value, real time: Production AI with Amazon SageMaker and Tecton Read More »

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 as Amazon Bedrock Knowledge Bases and …

Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models Read More »

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 is comparable to the largest and …

A guide to Amazon Bedrock Model Distillation (preview) Read More »

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 Bedrock IDE is now incorporated into …

Build generative AI applications quickly with Amazon Bedrock IDE in Amazon SageMaker Unified Studio Read More »

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 managing growing infrastructure and storage needs. …

Scale ML workflows with Amazon SageMaker Studio and Amazon SageMaker HyperPod Read More »