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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 363
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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 106
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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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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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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 87
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Handling Online-Offline Discrepancy in Pinterest Ads Ranking System

Pinterest Engineering

In particular, our machine learning powered ads ranking systems are trying to understand users’ engagement and conversion intent and promote the right ads to the right user at the right time. Our engineers are constantly discovering new algorithms and new signals to improve the performance of our machine learning models.

Systems 96
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Ensuring the Successful Launch of Ads on Netflix

Netflix Tech

We used this simulation to help us surface problems of scale and validate our Ads algorithms. Replay traffic enabled us to test our new systems and algorithms at scale before launch, while also making the traffic as realistic as possible. We also constructed and checked our ad monitoring and alerting system during this period.

Algorithm 139