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Designing Instagram

High Scalability

Design a photo-sharing platform similar to Instagram where users can upload their photos and share it with their followers. High Level Design. from a client it performs two parallel operations: i) persisting the action in the data store ii) publish the action in a streaming data store for a pub-sub model. Component Design.

Design 334
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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Since its inception , Metaflow has been designed to provide a human-friendly API for building data and ML (and today AI) applications and deploying them in our production infrastructure frictionlessly. Occasionally, these use cases involve terabytes of data, so we have to pay attention to performance.

Systems 226
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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

This blog post explores how AI observability enables organizations to predict and control costs, performance, and data reliability. Augmenting LLM input in this way reduces apparent knowledge gaps in the training data and limits AI hallucinations. RAG augments user prompts with relevant data retrieved from outside the LLM.

Cache 204
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Why growing AI adoption requires an AI observability strategy

Dynatrace

An AI observability strategy—which monitors IT system performance and costs—may help organizations achieve that balance. Training AI data is resource-intensive and costly, again, because of increased computational and storage requirements. AI performs frequent data transfers. Continuously monitor AI models’ performance.

Strategy 221
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Unmatched scalability and security of Dynatrace extensions now available for all supported technologies: 7 reasons to migrate your JMX and Python plugins

Dynatrace

are technologically very different, Python and JMX extensions designed for Extension Framework 1.0 Declarative extensions—written in a human-readable YAML format—require no coding skills and are inherently scalable and secure, thanks to the high-performance data sources that underpin them. We’ve added Python support to Extensions 2.0,

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Snuba: automating weak supervision to label training data

The Morning Paper

Snuba: automating weak supervision to label training data Varma & Ré, VLDB 2019. It’s tackling the same fundamental problem: how to gather enough labeled data to train a model, and how to effectively use it in a weak supervision setting (supervised learning with noisy labels). F1 points in terms of end model performance.

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Nurturing Design in Your Software Engineering Culture

Strategic Tech

There are a few qualities that differentiate average from high performing software engineering organisations. I believe that attitude towards the design of code and architecture is one of them. Both valuing design and striving for continuous delivery are necessary. So we need to make it part of everything we do.