AWS Machine Learning Blog Today, we’re excited to announce the availability of Meta Llama 3 inference on AWS Trainium and AWS Inferentia based instances in Amazon SageMaker JumpStart. The Meta Llama 3 models are a collection of pre-trained and fine-tuned generative text models. Amazon Elastic Compute Cloud (Amazon EC2) Trn1 and Inf2 instances, powered by […]Continue reading
AWS Machine Learning Blog Today, we announced the General Availability of Amazon Q, the most capable generative AI powered assistant for accelerating software development and leveraging companies’ internal data. “During the preview, early indications signaled Amazon Q could help our customers’ employees become more than 80% more productive at their jobs; and with the new […]Continue reading
AWS Machine Learning Blog At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With Knowledge Bases for Amazon Bedrock, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG). In previous posts, we covered new capabilities like hybrid […]Continue reading
AWS Machine Learning Blog At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With Knowledge Bases for Amazon Bedrock, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data using a fully managed Retrieval Augmented Generation (RAG) model. For RAG-based applications, the accuracy of the […]Continue reading
AWS Machine Learning Blog We’re excited to announce the availability of response streaming through Amazon SageMaker real-time inference. Now you can continuously stream inference responses back to the client when using SageMaker real-time inference to help you build interactive experiences for generative AI applications such as chatbots, virtual assistants, and music generators. With this new […]Continue reading
AWS Machine Learning Blog Today, we announce the availability of sample notebooks that demonstrate question answering tasks using a Retrieval Augmented Generation (RAG)-based approach with large language models (LLMs) in Amazon SageMaker JumpStart. Text generation using RAG with LLMs enables you to generate domain-specific text outputs by supplying specific external data as part of the […]Continue reading
AWS Machine Learning Blog On November 30, 2021, we announced the general availability of Amazon SageMaker Canvas, a visual point-and-click interface that enables business analysts to generate highly accurate machine learning (ML) predictions without having to write a single line of code. With Canvas, you can take ML mainstream throughout your organization so business analysts […]Continue reading
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