Remove Authentication Remove Big Data Remove Load Balancer
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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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Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning - AI

Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message. After it’s authenticated, the request is forwarded to another Lambda function that contains our core application logic. For Authentication Audience , select App URL , as shown in the following screenshot.

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Build a custom UI for Amazon Q Business

AWS Machine Learning - AI

The workflow includes the following steps: The user accesses the chatbot application, which is hosted behind an Application Load Balancer. After the user logs in, they’re redirected to the Amazon Cognito login page for authentication. Additionally, it creates and configures those services to run the end-to-end demonstration.

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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

Harnessing the power of big data has become increasingly critical for businesses looking to gain a competitive edge. However, managing the complex infrastructure required for big data workloads has traditionally been a significant challenge, often requiring specialized expertise.

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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. In this blog, we’ll compare the three leading public cloud providers, namely Amazon Web Services (AWS), Microsoft Azure and Google Cloud.

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How to configure clients to connect to Apache Kafka Clusters securely – Part 1: Kerberos

Cloudera

In this article we will explain how to configure clients to authenticate with clusters using different authentication mechanisms. Secured Apache Kafka clusters can be configured to enforce authentication using different methods, including the following: SSL – TLS client authentication. Kerberos Authentication.

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AWS – The Silver Lining of the Cloud

RapidValue

Some of their security features include Multi-factor authentication, private subnets, Isolate GovCloud and encrypted data. It provides tools such as Auto Scaling, AWS Tools and Elastic Load Balancing to reduce the time spent on a task. This ultimately makes them a reliable and secure cloud computing service.

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