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Optimizing Generative AI With Retrieval-Augmented Generation: Architecture, Algorithms, and Applications Overview

DZone

This article is intended for data scientists, AI researchers, machine learning engineers, and advanced practitioners in the field of artificial intelligence who have a solid grounding in machine learning concepts, natural language processing , and deep learning architectures.

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How platform engineering and IDP observability can accelerate developer velocity

Dynatrace

As organizations look to expand DevOps maturity, improve operational efficiency, and increase developer velocity, they are embracing platform engineering as a key driver. Platform engineering: Build for self-service Self-service deployment is a key attribute of platform engineering. “It makes them more productive.

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Enhancing Kubernetes cluster management key to platform engineering success

Dynatrace

Five of the most common include cluster instability, resource and cost management, security, observability, and stress on engineering teams. Engineering teams are overwhelmed with stuff to do.” The post Enhancing Kubernetes cluster management key to platform engineering success appeared first on Dynatrace news.

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Practical Process Automation With Bernd Ruecker [Podcast]

DZone

Process Automation is defined as “a centerpiece of digitalization efforts” – where workflow engines are used as “a vital building block in modern architectures.”

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Automating Success: Building a better developer experience with platform engineering

Dynatrace

When it comes to platform engineering, not only does observability play a vital role in the success of organizations’ transformation journeys—it’s key to successful platform engineering initiatives. The various presenters in this session aligned platform engineering use cases with the software development lifecycle.

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1. Streamlining Membership Data Engineering at Netflix with Psyberg

The Netflix TechBlog

By Abhinaya Shetty , Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions. Let’s dive in! What is late-arriving data? Some techniques we used were: 1.

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DevOps engineer tools: Deploy, test, evaluate, repeat

Dynatrace

As cloud-native, distributed architectures proliferate, the need for DevOps technologies and DevOps platform engineers has increased as well. DevOps engineer tools can help ease the pressure as environment complexity grows. ” What does a DevOps platform engineer do? .”

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