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High-performance computing on AWS

Xebia

How does High-Performance Computing on AWS differ from regular computing? HPC services on AWS Compute Technically you could design and build your own HPC cluster on AWS, it will work but you will spend time on plumbing and undifferentiated heavy lifting. AWS has two services to support your HPC workload.

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Build a contextual text and image search engine for product recommendations using Amazon Bedrock and Amazon OpenSearch Serverless

AWS Machine Learning - AI

In this post, we show how to build a contextual text and image search engine for product recommendations using the Amazon Titan Multimodal Embeddings model , available in Amazon Bedrock , with Amazon OpenSearch Serverless. Amazon SageMaker Studio – It is an integrated development environment (IDE) for machine learning (ML).

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Incorporate offline and online human – machine workflows into your generative AI applications on AWS

AWS Machine Learning - AI

An important aspect of developing effective generative AI application is Reinforcement Learning from Human Feedback (RLHF). RLHF is a technique that combines rewards and comparisons, with human feedback to pre-train or fine-tune a machine learning (ML) model.

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Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

The AWS Well-Architected Framework provides best practices and guidelines for designing and operating reliable, secure, efficient, and cost-effective systems in the cloud. This post explores the new enterprise-grade features for Knowledge Bases on Amazon Bedrock and how they align with the AWS Well-Architected Framework.

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Natural Language Processing & Machine Learning in Higher Education

Mentormate

In this article, we will discuss how MentorMate and our partner eLumen leveraged natural language processing (NLP) and machine learning (ML) for data-driven decision-making to tame the curriculum beast in higher education. Here, we will primarily focus on drawing insights from structured and unstructured (text) data.

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Build an internal SaaS service with cost and usage tracking for foundation models on Amazon Bedrock

AWS Machine Learning - AI

Because Amazon Bedrock is serverless, you don’t have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. You can deploy the solution in your own account using the AWS CDK.

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Build a contextual chatbot for financial services using Amazon SageMaker JumpStart, Llama 2 and Amazon OpenSearch Serverless with Vector Engine

AWS Machine Learning - AI

In addition, customers are looking for choices to select the most performant and cost-effective machine learning (ML) model and the ability to perform necessary customization (fine-tuning) to fit their business use cases. An OpenSearch Serverless collection. A SageMaker execution role with access to OpenSearch Serverless.