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Artificial Intelligence in Cloud Computing

Scalegrid

This article delves into the specifics of how AI optimizes cloud efficiency, ensures scalability, and reinforces security, providing a glimpse at its transformative role without giving away extensive details. Exploring artificial intelligence in cloud computing reveals a game-changing synergy.

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Seamlessly Swapping the API backend of the Netflix Android app

The Netflix TechBlog

At Netflix, we have adopted the Backend for Frontend (BFF) pattern : instead of having one general purpose “backend API”, we have one backend per client (Android/iOS/TV/web). This allowed Android engineers to have much more control and observability over how we get our data.

Latency 233
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Web Performance Bookshelf

Rigor

Reading time 1 min Why share the library of the web performance books while there’s a substantial collection of fantastic websites and articles on the net? This practical guide shows users how run tests & interpret results to gain a better and more thorough understanding of hidden features in WebPageTest. Mobile First.

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Kubernetes for Big Data Workloads

Abhishek Tiwari

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. better cluster resource utilization.

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The Netflix Cosmos Platform

The Netflix TechBlog

A Cosmos service This article will explain why we built Cosmos, how it works and share some of the things we have learned along the way. The centralized data model that had served us well when we were a small team became a liability. Our response was to create Cosmos, a platform for workflow-driven, media-centric microservices.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly

As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. Why: Data Makes It Different. Much has been written about struggles of deploying machine learning projects to production. This approach is not novel.

DevOps 138
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Break that big ball of mud!

Particular Software

This article was originally published on the NDC 2016 Blog. It's a place filled with unit tests, automated integration tests, and code written using the "most latest" framework in town. Events are nothing but simple data transfer objects (DTO) to convey something significant that happened within your business system.