Favorite Data contains information, and information can be used to predict future behaviors, from the buying habits of customers to securities returns. Businesses are seeking a competitive advantage by being able to use the data they hold, apply it to their unique understanding of their business domain, and then generate

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Shared by AWS Machine Learning September 12, 2024

Favorite We are thrilled to introduce Amazon Elastic Kubernetes Service (Amazon EKS) support in Amazon SageMaker HyperPod, a purpose-built infrastructure engineered with resilience at its core. This capability allows for the seamless addition of SageMaker HyperPod managed compute to EKS clusters, using automated node and job resiliency features for foundation

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Shared by AWS Machine Learning September 12, 2024

Favorite This post is co-written with Tom Dyer, Samuel Barnett and 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

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Shared by AWS Machine Learning September 11, 2024

Favorite 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

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Shared by AWS Machine Learning September 10, 2024

Favorite 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

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Shared by AWS Machine Learning September 10, 2024

Favorite 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

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Shared by AWS Machine Learning September 10, 2024

Favorite 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

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Shared by AWS Machine Learning September 7, 2024

Favorite 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

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Shared by AWS Machine Learning September 7, 2024

Favorite 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

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Shared by AWS Machine Learning September 7, 2024

Favorite As generative artificial intelligence (AI) continues to revolutionize every industry, the importance of effective prompt optimization through prompt engineering techniques has become key to efficiently balancing the quality of outputs, response time, and costs. Prompt engineering refers to the practice of crafting and optimizing inputs to the models by

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Shared by AWS Machine Learning September 6, 2024