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

AWS Machine Learning - AI

In this post, we explore a practical solution that uses Streamlit , a Python library for building interactive data applications, and AWS services like Amazon Elastic Container Service (Amazon ECS), Amazon Cognito , and the AWS Cloud Development Kit (AWS CDK) to create a user-friendly generative AI application with authentication and deployment.

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Securing a Web Application with AWS Application Load Balancer

Stackery

Editor’s note: while we love serverless at Stackery, there are still some tasks that will require the use of a virtual machine. If you’re still using an Elastic Compute Cloud (EC2) Virtual Machine, enjoy this very useful tutorial on load balancing. The protocol between the load balancer and the instance is HTTP on port 80.

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Are Cloud Serverless Functions Exposing Your Data?

Prisma Clud

More than 25% of all publicly accessible serverless functions have access to sensitive data , as seen in internal research. The question then becomes, Are cloud serverless functions exposing your data? Security Risks of Serverless as a Perimeter Choosing the right serverless offering entails operational and security considerations.

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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.

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

AWS Machine Learning - AI

It contains services used to onboard, manage, and operate the environment, for example, to onboard and off-board tenants, users, and models, assign quotas to different tenants, and authentication and authorization microservices. API Gateway is serverless and hence automatically scales with traffic.

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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 chatbot application container is built using Streamli t and fronted by an AWS Application Load Balancer (ALB).