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Build and deploy a UI for your generative AI applications with AWS and Python

AWS Machine Learning - AI

AWS provides a powerful set of tools and services that simplify the process of building and deploying generative AI applications, even for those with limited experience in frontend and backend development. The AWS deployment architecture makes sure the Python application is hosted and accessible from the internet to authenticated users.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning - AI

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. Load balancer – Another option is to use a load balancer that exposes an HTTPS endpoint and routes the request to the orchestrator.

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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

AWS offers powerful generative AI services , including Amazon Bedrock , which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. The following figure illustrates the high-level design of the solution.

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Responsible AI in action: How Data Reply red teaming supports generative AI safety on AWS

AWS Machine Learning - AI

At Data Reply and AWS, we are committed to helping organizations embrace the transformative opportunities generative AI presents, while fostering the safe, responsible, and trustworthy development of AI systems. Post-authentication, users access the UI Layer, a gateway to the Red Teaming Playground built on AWS Amplify and React.

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Revolutionizing customer service: MaestroQA’s integration with Amazon Bedrock for actionable insight

AWS Machine Learning - AI

We discuss the unique challenges MaestroQA overcame and how they use AWS to build new features, drive customer insights, and improve operational inefficiencies. Its serverless architecture allowed the team to rapidly prototype and refine their application without the burden of managing complex hardware infrastructure.

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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning - AI

That’s where the new Amazon EMR Serverless application integration in Amazon SageMaker Studio can help. In this post, we demonstrate how to leverage the new EMR Serverless integration with SageMaker Studio to streamline your data processing and machine learning workflows.

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Building Resilient Public Networking on AWS: Part 2

Xebia

Deploy Secure Public Web Endpoints Welcome to Building Resilient Public Networking on AWS—our comprehensive blog series on advanced networking strategies tailored for regional evacuation, failover, and robust disaster recovery. We laid the groundwork for understanding the essentials that underpin the forthcoming discussions.

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