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Establishing a Large Scale Learned Retrieval System at Pinterest

Pinterest Engineering

Modern large-scale recommendation systems usually include multiple stages where retrieval aims at retrieving candidates from billions of candidate pools, and ranking predicts which item a user tends to engage from the trimmed candidate set retrieved from early stages [2]. General multi-stage recommendation system design in Pinterest.

Systems 67
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An educational side project

The Pragmatic Engineer

Juraj included system monitoring parts which monitor the server’s capacity he runs the app on: The monitoring page on the Rides app And it doesn’t end here. Juraj created a systems design explainer on how he built this project, and the technologies used: The systems design diagram for the Rides application The app uses: Node.js

Education 364
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Why Open Table Format Architecture is Essential for Modern Data Systems

phData: Data Engineering

The world we live in today presents larger datasets, more complex data, and diverse needs, all of which call for efficient, scalable data systems. In this blog, we will discuss: What is the Open Table format (OTF)? These systems are built on open standards and offer immense analytical and transactional processing flexibility.

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How Meta discovers data flows via lineage at scale

Engineering at Meta

It is a critical and powerful tool for scalable discovery of relevant data and data flows, which supports privacy controls across Metas systems. In this blog, we will delve into an early stage in PAI implementation: data lineage. Data lineage enables us to efficiently navigate these assets and protect user data.

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Build Compound AI Systems Faster with Databricks Mosaic AI

databricks

Many of our customers are shifting from monolithic prompts with general-purpose models to specialized compound AI systems to achieve the quality needed for.

Systems 135
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Building a Question-Answering System Using RAG

WeCloudData

The ability to extract information from vast amounts of text has made question-answering (QA) systems essential in the modern era of AI-driven apps. RAG-based question-answering systems use large language models to generate human-like responses to user queries.

Systems 52
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Netflix’s Distributed Counter Abstraction

Netflix Tech

By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.

Datasets 101