Category: Compute

In this post, we walk through a multi-developer CI/CD pipeline for Amazon Lex that enables isolated development environments, automated testing, and streamlined deployments. We show you how to set up the solution and share real-world results from teams using this approach.

Organizations find it challenging to implement a secure embedded chat in their applications and can require weeks of development to build authentication, token validation, domain security, and global distribution infrastructure. In this post, we show you how to solve this with a one-click deployment solution to embed the chat agents using the Quick Suite Embedding SDK in enterprise portals.

In this post, we explain how you can use the Flyte Python SDK to orchestrate and scale AI/ML workflows. We explore how the Union.ai 2.0 system enables deployment of Flyte on Amazon Elastic Kubernetes Service (Amazon EKS), integrating seamlessly with AWS services like Amazon Simple Storage Service (Amazon S3), Amazon Aurora, AWS Identity and Access Management (IAM), and Amazon CloudWatch. We explore the solution through an AI workflow example, using the new Amazon S3 Vectors service.

In this post, we show how to create an AI-powered recruitment system using Amazon Bedrock, Amazon Bedrock Knowledge Bases, AWS Lambda, and other AWS services to enhance job description creation, candidate communication, and interview preparation while maintaining human oversight.

In this post, we discuss how Amazon Nova in Amazon Bedrock can be used to implement an AI-powered image recognition solution that automates the detection and validation of module components, significantly reducing manual verification efforts and improving accuracy.

In this post, we demonstrate how to use the CLI and the SDK to create and manage SageMaker HyperPod clusters in your AWS account. We walk through a practical example and dive deeper into the user workflow and parameter choices.

In this post, we illustrate how Clarus Care, a healthcare contact center solutions provider, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a generative AI-powered contact center prototype. This solution enables conversational interaction and multi-intent resolution through an automated voicebot and chat interface. It also incorporates a scalable service model to support growth, human transfer capabilities–when requested or for urgent cases–and an analytics pipeline for performance insights.

In this post, we demonstrate how CLICKFORCE used AWS services to build Lumos and transform advertising industry analysis from weeks-long manual work into an automated, one-hour process.

In this post, we explore the architecture that AutoScout24 used to build their standardized AI development framework, enabling rapid deployment of secure and scalable AI agents.

In this post, we will walk through the performance constraints and design choices by OARC and REMAP teams at UCLA, including how AWS serverless infrastructure, AWS Managed Services, and generative AI services supported the rapid design and deployment of our solution. We will also describe our use of Amazon SageMaker AI and how it can be used reliably in immersive live experiences.