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Architectural Insights: Designing Efficient Multi-Layered Caching With Instagram Example

DZone

Caching is a critical technique for optimizing application performance by temporarily storing frequently accessed data, allowing for faster retrieval during subsequent requests. Multi-layered caching involves using multiple levels of cache to store and retrieve data.

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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

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. For many of our applications, model explainability matters.

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What is a Distributed Storage System

Scalegrid

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. This guide delves into how these systems work, the challenges they solve, and their essential role in businesses and technology.

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How Caches Become Databases – And Why You Don’t Want This

VoltDB

The most obvious and common way this happens is when companies try to evolve their caches into a data platform that can, for example, be used as highly available enterprise key-value stores for volatile data. Let’s look at a typical scenario involving the javax cache API, also known as JSR107. How hard can it be?

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Cache and Prizes

Alex Russell

Browsers will cache tools popular among vocal, leading-edge developers. There's plenty of space for caching most popular frameworks. The best available proxy data also suggests that shared caches would have a minimal positive effect on performance. Browsers now understand the classic shared HTTP cache behaviour as a privacy bug.

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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

GenAI is prone to erratic behavior due to unforeseen data scenarios or underlying system issues. For example, a Stanford University and UC Berkeley team noted in a research study that ChatGPT behavior deteriorates over time. Failure to provide timely and accurate answers erodes user trust, hinders adoption, and harms retention.

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Improved Alerting with Atlas Streaming Eval

The Netflix TechBlog

Engineers want their alerting system to be realtime, reliable, and actionable. A few years ago, we were paged by our SRE team due to our Metrics Alerting System falling behind — critical application health alerts reached engineers 45 minutes late! In other words, false positives are bad but false negatives are the absolute worst!

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