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Large language model inference over confidential data using AWS Nitro Enclaves

AWS Machine Learning Blog

In this post, we discuss how Leidos worked with AWS to develop an approach to privacy-preserving large language model (LLM) inference using AWS Nitro Enclaves. LLMs are designed to understand and generate human-like language, and are used in many industries, including government, healthcare, financial, and intellectual property.

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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning Blog

Scalability and performance – The EMR Serverless integration automatically scales the compute resources up or down based on your workload’s demands, making sure you always have the necessary processing power to handle your big data tasks. This flexibility helps optimize performance and minimize the risk of bottlenecks or resource constraints.

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Build high-performance ML models using PyTorch 2.0 on AWS – Part 1

AWS Machine Learning Blog

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. release, AWS customers can now do same things as they could with PyTorch 1.x 24xlarge with AWS PyTorch 2.0

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Build a multilingual automatic translation pipeline with Amazon Translate Active Custom Translation

AWS Machine Learning Blog

We demonstrate how to use the AWS Management Console and Amazon Translate public API to deliver automatic machine batch translation, and analyze the translations between two language pairs: English and Chinese, and English and Spanish. In this post, we present a solution that D2L.ai

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Build a serverless voice-based contextual chatbot for people with disabilities using Amazon Bedrock

AWS Machine Learning Blog

At Amazon and AWS, we are always finding innovative ways to build inclusive technology. We demonstrate the process of integrating Anthropic Claude’s advanced natural language processing capabilities with the serverless architecture of Amazon Bedrock, enabling the deployment of a highly scalable and cost-effective solution.

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

IAM role – SageMaker requires an AWS Identity and Access Management (IAM) role to be assigned to a SageMaker Studio domain or user profile to manage permissions effectively. Create database connections The built-in SQL browsing and execution capabilities of SageMaker Studio are enhanced by AWS Glue connections. or later image versions.

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AI-powered code suggestions and security scans in Amazon SageMaker notebooks using Amazon CodeWhisperer and Amazon CodeGuru

AWS Machine Learning Blog

These AI-powered extensions help accelerate ML development by offering code suggestions as you type, and ensure that your code is secure and follows AWS best practices. Additionally, make sure you have appropriate access to both CodeWhisperer and CodeGuru using AWS Identity and Access Management (IAM).

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