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Architect a mature generative AI foundation on AWS

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Scaling and load balancing The gateway can handle load balancing across different servers, model instances, or AWS Regions so that applications remain responsive. The AWS Solutions Library offers solution guidance to set up a multi-provider generative AI gateway. Leave us a comment and we will be glad to collaborate.

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Build a conversational data assistant, Part 1: Text-to-SQL with Amazon Bedrock Agents

AWS Machine Learning Blog

Large language models can transform how we bridge the gap between business questions and actionable data insights. For most organizations, this gap remains stubbornly wide, with business teams trapped in endless cycles—decoding metric definitions and hunting for the correct data sources to manually craft each SQL query.

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Create a generative AI-based application builder assistant using Amazon Bedrock Agents

AWS Machine Learning Blog

Amazon Bedrock Agents is instrumental in customization and tailoring apps to help meet specific project requirements while protecting private data and securing their applications. These agents work with AWS managed infrastructure capabilities and Amazon Bedrock , reducing infrastructure management overhead.

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Build a conversational data assistant, Part 2 – Embedding generative business intelligence with Amazon Q in QuickSight

AWS Machine Learning Blog

This creates a seamless bridge between learning about metrics and visualizing them—users can start with simple queries about metric definitions and quickly transition to data exploration without reformulating their requests. Lakshdeep Vatsa is a Senior Data Engineer within the Amazon Worldwide Returns and ReCommerce Data Services team.

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

AWS Machine Learning Blog

In addition to its groundbreaking AI innovations, Zeta Global has harnessed Amazon Elastic Container Service (Amazon ECS) with AWS Fargate to deploy a multitude of smaller models efficiently. It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines.

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

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Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. To do this, we provide an AWS CloudFormation template to create a stack that contains the resources.

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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning Blog

Designing the prompt Before starting any scaled use of generative AI, you should have the following in place: A clear definition of the problem you are trying to solve along with the end goal. If prompted, set up a user profile for SageMaker Studio by providing a user name and specifying AWS Identity and Access Management (IAM) permissions.

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