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AI-Driven API and Microservice Architecture Design for Cloud

Dzone - DevOps

Incorporating AI into API and microservice architecture design for the Cloud can bring numerous benefits. Automated scaling : AI can monitor usage patterns and automatically scale microservices to meet varying demands, ensuring efficient resource utilization and cost-effectiveness.

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5 You’re Probably F**king Up Your Microservices

OverOps

It seems like everyone is into microservices these days, and monolith architectures are slowly fading into obscurity. With Microservices, though, there seems to be more consensus that the trend is here to stay. With Microservices, though, there seems to be more consensus that the trend is here to stay. It makes sense.

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Debugging Distributed Systems: 3 Common Distributed Tracing Challenges & How to Overcome Them

OverOps

Below we outline common approaches to distributed tracing, the challenges these methods pose and how OverOps can help deliver greater insights when troubleshooting across microservices. The accelerated adoption of microservices and increasingly distributed systems brings the promise of greater speed, scalability and flexibility.

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The Challenge of Scaling Metrics in Distributed Systems

DevOps.com

Whether migrating from a monolithic architecture or building a distributed system from ground zero, there are many benefits to leverage from a microservices architecture–faster software deployment cycles, enhanced scalability, improved isolation of risk. The […]. The […].

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Monitoring AWS Container Environments at Scale

Particularly well-suited for microservice-oriented architectures and agile workflows, containers help organizations improve developer efficiency, feature velocity, and optimization of resources. Key metrics to monitor when leveraging two container orchestration systems. Containers power many of the applications we use every day.

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Supporting Diverse ML Systems at Netflix

Netflix Tech

The Machine Learning Platform (MLP) team at Netflix provides an entire ecosystem of tools around Metaflow , an open source machine learning infrastructure framework we started, to empower data scientists and machine learning practitioners to build and manage a variety of ML systems. ETL workflows), as well as downstream (e.g.

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Metrics Matter: The 4 Types of Code-Level Data OverOps Collects

OverOps

To answer this question, we recently created a framework that helps organizations pinpoint critical gaps in data and metrics that are holding them back on their reliability journeys. Code Metrics. Transactions & Performance Metrics. System Metrics. Are there any blocked threads related to this failure?

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