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

AWS Machine Learning - AI

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. You can use AWS services such as Application Load Balancer to implement this approach.

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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. You can also fine-tune your choice of Amazon Bedrock model to balance accuracy and speed.

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Building Resilient Public Networking on AWS: Part 2

Xebia

Fargate Cluster: Establishes the Elastic Container Service (ECS) in AWS, providing a scalable and serverless container execution environment. Public Application Load Balancer (ALB): Establishes an ALB, integrating the previous SSL/TLS certificate for enhanced security. The ALB serves as the entry point for our web container.

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning - AI

If you’re implementing complex RAG applications into your daily tasks, you may encounter common challenges with your RAG systems such as inaccurate retrieval, increasing size and complexity of documents, and overflow of context, which can significantly impact the quality and reliability of generated answers.

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

AWS Machine Learning - AI

Our solution uses an FSx for ONTAP file system as the source of unstructured data and continuously populates an Amazon OpenSearch Serverless vector database with the user’s existing files and folders and associated metadata. The RAG Retrieval Lambda function stores conversation history for the user interaction in an Amazon DynamoDB table.

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AWS vs. Azure vs. Google Cloud: Comparing Cloud Platforms

Kaseya

In addition, you can also take advantage of the reliability of multiple cloud data centers as well as responsive and customizable load balancing that evolves with your changing demands. Cloud adoption also provides businesses with flexibility and scalability by not restricting them to the physical limitations of on-premises servers.