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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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Bring Your Own Algorithm to Anomaly Detection

Pinterest Engineering

Charles Wu | Software Engineer; Isabel Tallam | Software Engineer; Kapil Bajaj | Engineering Manager Overview In this blog, we present a pragmatic way of integrating analytics, written in Python, with our distributed anomaly detection platform, written in Java. The execution flow of one anomaly detection job, defined by one JSON job spec.

Algorithm 110
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The Top Pinterest Engineering Blog posts from 2023

Pinterest Engineering

Our Pinterest Engineering Blog goes deeper into the technical learnings and insights behind many of these launches. Here, you’ll be the first to know about new Engineering blogs, events and employee stories. Thank you for supporting our Pinterest Engineering Blog this year. Cheers to 2024!

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What are the Commonly Used Machine Learning Algorithms?

Knowledge Hut

There is no end to what can be achieved with the right ML algorithm. Machine Learning is comprised of different types of algorithms, each of which performs a unique task. U sers deploy these algorithms based on the problem statement and complexity of the problem they deal with.

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Real-Time AI for Crisis Management: Responding Faster with Smarter Systems

Striim

By integrating AI/ML models directly into these data streams, organizations gain deeper insights: advanced algorithms can spot emerging patterns, predict cascading effects, and recommend interventionsall in the moment. Systems must be capable of handling high-velocity data without bottlenecks.

Systems 52
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The Recommendation System at Lyft

Lyft Engineering

This blog post focuses on the scope and the goals of the recommendation system, and explores some of the most recent changes the Rider team has made to better serve Lyft’s riders. Introduction: Scope of the Recommendation System The recommendation system covers user experiences throughout the ride journey.

Systems 88