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Building Resilient Public Networking on AWS: Part 4

Xebia

Region Evacuation with static anycast IP approach Welcome back to our comprehensive "Building Resilient Public Networking on AWS" blog series, where we delve into advanced networking strategies for regional evacuation, failover, and robust disaster recovery. Find the detailed guide here.

AWS 130
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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. Load balancer – Another option is to use a load balancer that exposes an HTTPS endpoint and routes the request to the orchestrator. API Gateway also provides a WebSocket API.

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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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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.

AWS 147
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AWS Disaster Recovery Strategies – PoC with Terraform

Xebia

A regional failure is an uncommon event in AWS (and other Public Cloud providers), where all Availability Zones (AZs) within a region are affected by any condition that impedes the correct functioning of the provisioned Cloud infrastructure. The code is publicly available on the links below, with how-to-use documentation. Strategies.

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

AWS Machine Learning - AI

Reduced operational overhead – The EMR Serverless integration with AWS streamlines big data processing by managing the underlying infrastructure, freeing up your team’s time and resources. Runtime roles are AWS Identity and Access Management (IAM) roles that you can specify when submitting a job or query to an EMR Serverless application.