Favorite Today, physicians spend about 49% of their workday documenting clinical visits, which impacts physician productivity and patient care. Did you know that for every eight hours that office-based physicians have scheduled with patients, they spend more than five hours in the EHR? As a consequence, healthcare practitioners exhibit a

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Shared by AWS Machine Learning August 22, 2024

Favorite Generative artificial intelligence (AI), particularly Retrieval Augmented Generation (RAG) solutions, are rapidly demonstrating their vast potential to revolutionize enterprise operations. RAG models combine the strengths of information retrieval systems with advanced natural language generation, enabling more contextually accurate and informative outputs. From automating customer interactions to optimizing backend operation

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Shared by AWS Machine Learning August 22, 2024

Favorite 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

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Shared by AWS Machine Learning August 22, 2024

Favorite 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

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Shared by AWS Machine Learning August 22, 2024

Favorite 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

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Shared by AWS Machine Learning August 22, 2024

Favorite One of the most common applications of generative artificial intelligence (AI) and large language models (LLMs) in an enterprise environment is answering questions based on the enterprise’s knowledge corpus. Pre-trained foundation models (FMs) excel at natural language understanding (NLU) tasks, including summarization, text generation, and question answering across a

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Shared by AWS Machine Learning August 21, 2024

Favorite Amazon SageMaker Ground Truth significantly reduces the cost and time required for labeling data by integrating human annotators with machine learning to automate the labeling process. You can use SageMaker Ground Truth to create labeling jobs, which are workflows where data objects (such as images, videos, or documents) need to

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Shared by AWS Machine Learning August 21, 2024

Favorite Amazon SageMaker Data Wrangler provides a visual interface to streamline and accelerate data preparation for machine learning (ML), which is often the most time-consuming and tedious task in ML projects. Amazon SageMaker Canvas is a low-code no-code visual interface to build and deploy ML models without the need to

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Shared by AWS Machine Learning August 21, 2024

Favorite The Cohere Rerank 3 Nimble foundation model (FM) is now generally available in Amazon SageMaker JumpStart. This model is the newest FM in Cohere’s Rerank model series, built to enhance enterprise search and Retrieval Augmented Generation (RAG) systems. In this post, we discuss the benefits and capabilities of this

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Shared by AWS Machine Learning August 19, 2024

Favorite QnABot on AWS (an AWS Solution) now provides access to Amazon Bedrock foundational models (FMs) and Knowledge Bases for Amazon Bedrock, a fully managed end-to-end Retrieval Augmented Generation (RAG) workflow. You can now provide contextual information from your private data sources that can be used to create rich, contextual,

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Shared by AWS Machine Learning August 15, 2024