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Amazon SageMaker unveils the Cohere Command R fine-tuning model

Amazon SageMaker unveils the Cohere Command R fine-tuning model

AWS announced the availability of the Cohere Command R fine-tuning model on Amazon SageMaker. This latest addition to the SageMaker suite of machine learning (ML) capabilities empowers enterprises to harness the power of large language models (LLMs) and unlock their full potential for a wide range of applications. Cohere Command R is a scalable, frontier …

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Derive meaningful and actionable operational insights from AWS Using Amazon Q Business

Derive meaningful and actionable operational insights from AWS Using Amazon Q Business

As a customer, you rely on Amazon Web Services (AWS) expertise to be available and understand your specific environment and operations. Today, you might implement manual processes to summarize lessons learned, obtain recommendations, or expedite the resolution of an incident. This can be time consuming, inconsistent, and not readily accessible. This post shows how to …

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Accelerate your generative AI distributed training workloads with the NVIDIA NeMo Framework on Amazon EKS

Accelerate your generative AI distributed training workloads with the NVIDIA NeMo Framework on Amazon EKS

In today’s rapidly evolving landscape of artificial intelligence (AI), training large language models (LLMs) poses significant challenges. These models often require enormous computational resources and sophisticated infrastructure to handle the vast amounts of data and complex algorithms involved. Without a structured framework, the process can become prohibitively time-consuming, costly, and complex. Enterprises struggle with managing …

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Governing the ML lifecycle at scale, Part 2: Multi-account foundations

Governing the ML lifecycle at scale, Part 2: Multi-account foundations

Your multi-account strategy is the core of your foundational environment on AWS. Design decisions around your multi-account environment are critical for operating securely at scale. Grouping your workloads strategically into multiple AWS accounts enables you to apply different controls across workloads, track cost and usage, reduce the impact of account limits, and mitigate the complexity …

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Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

This post is co-written with MagellanTV and Mission Cloud.  Video dubbing, or content localization, is the process of replacing the original spoken language in a video with another language while synchronizing audio and video. Video dubbing has emerged as a key tool in breaking down linguistic barriers, enhancing viewer engagement, and expanding market reach. However, …

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How Mixbook used generative AI to offer personalized photo book experiences

How Mixbook used generative AI to offer personalized photo book experiences

This post is co-written with Vlad Lebedev and DJ Charles from Mixbook. Mixbook is an award-winning design platform that gives users unrivaled creative freedom to design and share one-of-a-kind stories, transforming the lives of more than six million people. Today, Mixbook is the #1 rated photo book service in the US with 26 thousand five-star …

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Using Agents for Amazon Bedrock to interactively generate infrastructure as code

Using Agents for Amazon Bedrock to interactively generate infrastructure as code

In the diverse toolkit available for deploying cloud infrastructure, Agents for Amazon Bedrock offers a practical and innovative option for teams looking to enhance their infrastructure as code (IaC) processes. Agents for Amazon Bedrock automates the prompt engineering and orchestration of user-requested tasks. After being configured, an agent builds the prompt and augments it with …

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Improve RAG accuracy with fine-tuned embedding models on Amazon SageMaker

Improve RAG accuracy with fine-tuned embedding models on Amazon SageMaker

Retrieval Augmented Generation (RAG) is a popular paradigm that provides additional knowledge to large language models (LLMs) from an external source of data that wasn’t present in their training corpus. RAG provides additional knowledge to the LLM through its input prompt space and its architecture typically consists of the following components: Indexing: Prepare a corpus …

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