Remove Azure Remove DevOps Remove Load Balancer Remove Performance
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AI-Driven API and Microservice Architecture Design for Cloud

Dzone - DevOps

Here are some key aspects where AI can drive improvements in architecture design: Intelligent planning : AI can assist in designing the architecture by analyzing requirements, performance metrics, and best practices to recommend optimal structures for APIs and microservices.

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Building Resilient Public Networking on AWS: Part 2

Xebia

Public Application Load Balancer (ALB): Establishes an ALB, integrating the previous SSL/TLS certificate for enhanced security. It’s important to note that, for the sake of clarity, we’ll be performing these actions manually. Our aim is to provide clarity by explaining each step in detail.

AWS 147
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Hybrid vs. Multi-cloud: The Good, the Bad and the Network Observability Needed

Kentik

The public clouds (representing Google, AWS, IBM, Azure, Alibaba and Oracle) are all readily available. This allows DevOps teams to configure the application to increase or decrease the amount of system capacity, like CPU, storage, memory and input/output bandwidth, all on-demand. Moving to the cloud can also increase performance.

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Everything You Need to Optimize Your Sitefinity Project Is in Sitefinity Cloud

Progress

All the tools are there too to sustain and consistently improve performance because, hey, we’re all in it for the long run. Identify performance bottlenecks? Address and resolve issues and optimize your project for stellar performance? Azure Monitor and Application Insights. But it may well be where the fun starts.

Cloud 52
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Everything You Need to Optimize Your Sitefinity Project Is in Sitefinity Cloud

Progress

All the tools are there too to sustain and consistently improve performance because, hey, we’re all in it for the long run. Identify performance bottlenecks? Address and resolve issues and optimize your project for stellar performance? Azure Monitor and Application Insights. But it may well be where the fun starts.

Cloud 52
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Introducing the MLOps Management Agent

DataRobot

In several earlier blog posts, we have focused on what we at DataRobot call the AI production gap , which refers to the gap that makes it difficult to transition models from the data science teams who develop them to the IT and DevOps teams who are responsible for deploying and monitoring them in production.

Azure 52
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Leveraging cloud-native managed services for long-term benefits

Capgemini

Traditionally, managed-services providers have focused on running and operating on-premises infrastructures and, more recently, handling IaaS for cloud providers like AWS and Microsoft Azure. But because of the traditional separation between infrastructure and application groups, these teams have operated in a siloed fashion.

Cloud 52