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Tag: Amazon SageMaker JumpStart

Advanced RAG patterns on Amazon SageMaker | Amazon Web Services

Today, customers of all industries—whether it’s financial services, healthcare and life sciences, travel and hospitality, media and entertainment, telecommunications, software as a service (SaaS),...

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Best practices to build generative AI applications on AWS | Amazon Web Services

Generative AI applications driven by foundational models (FMs) are enabling organizations with significant business value in customer experience, productivity, process optimization, and innovations. However,...

Gemma is now available in Amazon SageMaker JumpStart  | Amazon Web Services

Today, we’re excited to announce that the Gemma model is now available for customers using Amazon SageMaker JumpStart. Gemma is a family of language models based on...

Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation | Amazon Web Services

AI’s growing influence in large organizations brings crucial challenges in managing AI platforms. These include developing a scalable and operationally efficient platform that adheres...

Code Llama 70B is now available in Amazon SageMaker JumpStart | Amazon Web Services

Today, we are excited to announce that Code Llama foundation models, developed by Meta, are available for customers through Amazon SageMaker JumpStart to deploy...

Accenture creates a regulatory document authoring solution using AWS generative AI services | Amazon Web Services

This post is co-written with Ilan Geller, Shuyu Yang and Richa Gupta from Accenture. Bringing innovative new pharmaceuticals drugs to market is a long...

Deploy large language models for a healthtech use case on Amazon SageMaker | Amazon Web Services

In 2021, the pharmaceutical industry generated $550 billion in US revenue. Pharmaceutical companies sell a variety of different, often novel, drugs on the market,...

Monitor embedding drift for LLMs deployed from Amazon SageMaker JumpStart | Amazon Web Services

One of the most useful application patterns for generative AI workloads is Retrieval Augmented Generation (RAG). In the RAG pattern, we find pieces of...

Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 1 | Amazon Web Services

With the advent of generative AI, today’s foundation models (FMs), such as the large language models (LLMs) Claude 2 and Llama 2, can perform...

Benchmark and optimize endpoint deployment in Amazon SageMaker JumpStart  | Amazon Web Services

When deploying a large language model (LLM), machine learning (ML) practitioners typically care about two measurements for model serving performance: latency, defined by the...

Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs | Amazon Web Services

Generative artificial intelligence (AI) applications built around large language models (LLMs) have demonstrated the potential to create and accelerate economic value for businesses. Examples...

Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector database on AWS Marketplace | Amazon Web Services

Generative AI solutions have the potential to transform businesses by boosting productivity and improving customer experiences, and using large language models (LLMs) with these...

Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning | Amazon Web Services

In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model...

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