AI

Revolutionize logo design creation with Amazon Bedrock: Embracing generative art, dynamic logos, and AI collaboration

In the field of technology and creative design, logo design and creation has adapted and evolved at a rapid pace. From the hieroglyphs of ancient Egypt to the sleek minimalism of today’s tech giants, the visual identities that define our favorite brands have undergone a remarkable transformation. Today, the world of creative design is once

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Reinvent personalization with generative AI on Amazon Bedrock using task decomposition for agentic workflows

Personalization has become a cornerstone of delivering tangible benefits to businesses and their customers. Generative AI and large language models (LLMs) offer new possibilities, although some businesses might hesitate due to concerns about consistency and adherence to company guidelines. This post presents an automated personalization solution that balances the innovative capabilities of LLMs with adherence

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Accelerate pre-training of Mistral’s Mathstral model with highly resilient clusters on Amazon SageMaker HyperPod

In recent years, FM sizes have been increasing. It is important to consider the massive amount of compute often required to train these models. The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia, custom machine learning (ML) chips designed

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Building an efficient MLOps platform with OSS tools on Amazon ECS with AWS Fargate

This post has been co-written with Artem Sysuev, Danny Portman, Matúš Chládek, and Saurabh Gupta from Zeta Global. Zeta Global is a leading data-driven, cloud-based marketing technology company that empowers enterprises to acquire, grow and retain customers. The company’s Zeta Marketing Platform (ZMP) is the largest omnichannel marketing platform with identity data at its core.

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Build RAG-based generative AI applications in AWS using Amazon FSx for NetApp ONTAP with Amazon Bedrock

The post is co-written with Michael Shaul and Sasha Korman from NetApp. Generative artificial intelligence (AI) applications are commonly built using a technique called Retrieval Augmented Generation (RAG) that provides foundation models (FMs) access to additional data they didn’t have during training. This data is used to enrich the generative AI prompt to deliver more

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