NotebookLM now lets you listen to a conversation about your sources
NotebookLM is releasing Audio Overviews, which turns your sources into an engaging discussion.
Enabling complex generative AI applications with Amazon Bedrock Agents
In June, I started a series of posts that highlight the key factors that are driving customers to choose Amazon Bedrock. The first covered building generative AI apps securely with Amazon Bedrock, while the second explored building custom generative AI applications with Amazon Bedrock. Now I’d like to take a closer look at Amazon Bedrock …
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Genomics England uses Amazon SageMaker to predict cancer subtypes and patient survival from multi-modal data
This post is co-written with Francisco Azuaje from Genomics England. Genomics England analyzes sequenced genomes for The National Health Service (NHS) in the United Kingdom, and then equips researchers to use data to advance biological research. As part of its goal to help people live longer, healthier lives, Genomics England is interested in facilitating more …
Align Meta Llama 3 to human preferences with DPO, Amazon SageMaker Studio, and Amazon SageMaker Ground Truth
Large language models (LLMs) have remarkable capabilities. Nevertheless, using them in customer-facing applications often requires tailoring their responses to align with your organization’s values and brand identity. In this post, we demonstrate how to use direct preference optimization (DPO), a technique that allows you to fine-tune an LLM with human preference data, together with Amazon …
Amazon EC2 P5e instances are generally available
State-of-the-art generative AI models and high performance computing (HPC) applications are driving the need for unprecedented levels of compute. Customers are pushing the boundaries of these technologies to bring higher fidelity products and experiences to market across industries. The size of large language models (LLMs), as measured by the number of parameters, has grown exponentially …
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Exploring data using AI chat at Domo with Amazon Bedrock
This post is co-written with Joe Clark from Domo. Data insights are crucial for businesses to enable data-driven decisions, identify trends, and optimize operations. Traditionally, gaining these insights required skilled analysts using specialized tools, which can make the process slow and less accessible. Generative artificial intelligence (AI) has revolutionized this by allowing users to interact …
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How Vidmob is using generative AI to transform its creative data landscape
This post was co-written with Mickey Alon from Vidmob. Generative artificial intelligence (AI) can be vital for marketing because it enables the creation of personalized content and optimizes ad targeting with predictive analytics. Specifically, such data analysis can result in predicting trends and public sentiment while also personalizing customer journeys, ultimately leading to more effective …
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Fine-tune Llama 3 for text generation on Amazon SageMaker JumpStart
Generative artificial intelligence (AI) models have become increasingly popular and powerful, enabling a wide range of applications such as text generation, summarization, question answering, and code generation. However, despite their impressive capabilities, these models often struggle with domain-specific tasks or use cases due to their general training data. To address this challenge, fine-tuning these models …
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Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval
Generative artificial intelligence (AI) applications powered by large language models (LLMs) are rapidly gaining traction for question answering use cases. From internal knowledge bases for customer support to external conversational AI assistants, these applications use LLMs to provide human-like responses to natural language queries. However, building and deploying such assistants with responsible AI best practices …
Build powerful RAG pipelines with LlamaIndex and Amazon Bedrock
This post was co-written with Jerry Liu from LlamaIndex. Retrieval Augmented Generation (RAG) has emerged as a powerful technique for enhancing the capabilities of large language models (LLMs). By combining the vast knowledge stored in external data sources with the generative power of LLMs, RAG enables you to tackle complex tasks that require both knowledge …
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