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At AWS, we are transforming our seller and customer journeys by using generative artificial intelligence (AI) across the sales lifecycle. We envision a future where AI seamlessly integrates into our teams’ workflows, automating repetitive tasks, providing intelligent recommendations, and freeing up time for more strategic, high-value interactions. Our field organization includes customer-facing teams (account managers, …

AWS empowers sales teams using generative AI solution built on Amazon Bedrock Read More »

Amazon Q Business is a conversational assistant powered by generative artificial intelligence (AI) that enhances workforce productivity by answering questions and completing tasks based on information in your enterprise systems, which each user is authorized to access. In an earlier post, we discussed how you can build private and secure enterprise generative AI applications with …

Build private and secure enterprise generative AI applications with Amazon Q Business using IAM Federation Read More »

This post is co-written with Sneha Godbole and Kate Riordan from Verisk. Verisk (Nasdaq: VRSK) is a leading strategic data analytics and technology partner to the global insurance industry. It empowers its customers to strengthen operating efficiency, improve underwriting and claims outcomes, combat fraud, and make informed decisions about global risks, including climate change, extreme …

Unleashing the power of generative AI: Verisk’s Discovery Navigator revolutionizes medical record review Read More »

Amazon Q Business is a generative artificial intelligence (AI)-powered assistant designed to enhance enterprise operations. It’s a fully managed service that helps provide accurate answers to users’ questions while honoring the security and access restrictions of the content. It can be tailored to your specific business needs by connecting to your company’s information and enterprise …

Index your Atlassian Confluence Cloud contents using the Amazon Q Confluence Cloud connector for Amazon Q Business Read More »

This post is co-written with Matt Marzillo from Snowflake. Today, we are excited to announce that the Snowflake Arctic Instruct model is available through Amazon SageMaker JumpStart to deploy and run inference. Snowflake Arctic is a family of enterprise-grade large language models (LLMs) built by Snowflake to cater to the needs of enterprise users, exhibiting …

Snowflake Arctic models are now available in Amazon SageMaker JumpStart Read More »

Building a deployment pipeline for generative artificial intelligence (AI) applications at scale is a formidable challenge because of the complexities and unique requirements of these systems. Generative AI models are constantly evolving, with new versions and updates released frequently. This makes managing and deploying these updates across a large-scale deployment pipeline while providing consistency and …

Fine tune a generative AI application for Amazon Bedrock using Amazon SageMaker Pipeline decorators Read More »

As developers continue to develop and deploy AI applications at scale across organizations, Azure is committed to delivering unprecedented choice in models as well as a flexible and comprehensive toolchain to handle the unique, complex and diverse needs of modern enterprises. This powerful combination of the latest models and cutting-edge tooling empowers developers to create …

Boost your AI with Azure’s new Phi model, streamlined RAG, and custom generative AI models Read More »

Today, we are excited to announce general availability of batch inference for Amazon Bedrock. This new feature enables organizations to process large volumes of data when interacting with foundation models (FMs), addressing a critical need in various industries, including call center operations. Call center transcript summarization has become an essential task for businesses seeking to …

Enhance call center efficiency using batch inference for transcript summarization with Amazon Bedrock Read More »

Fine-tuning Meta Llama 3.1 models with Amazon SageMaker JumpStart enables developers to customize these publicly available foundation models (FMs). The Meta Llama 3.1 collection represents a significant advancement in the field of generative artificial intelligence (AI), offering a range of capabilities to create innovative applications. The Meta Llama 3.1 models come in various sizes, with …

Fine-tune Meta Llama 3.1 models for generative AI inference using Amazon SageMaker JumpStart Read More »

Customer reviews can reveal customer experiences with a product and serve as an invaluable source of information to the product teams. By continually monitoring these reviews over time, businesses can recognize changes in customer perceptions and uncover areas of improvement. Analyzing these reviews to extract actionable insights enables data-driven decisions that can enhance customer experience …

Analyze customer reviews using Amazon Bedrock Read More »