The Rise (and Lessons Learned) of ML Models to Personalize Content on Home (Part I) - Spotify Engineering
The Rise (and Lessons Learned) of ML Models to Personalize Content on Home (Part I) At Spotify, our goal is to connect listeners with creators, and one way we do that is by recommending quality music and podcasts on the Home page. In this two-part blog series, we will talk about the ML models we build and use to recommend diverse and fulfilling content to our listeners, and the lessons we’ve learn
Big Data Processing at Spotify: The Road to Scio (Part 2) - Spotify Engineering
In this part we’ll take a closer look at Scio, including basic concepts, its unique features, and concrete use cases here at Spotify. Basic Concepts Scio is a Scala API for Apache Beam and Google Cloud Dataflow. It was designed as a thin wrapper on top of Beam’s Java SDK, while offering an easy way to build data pipelines in idiomatic Scala style. We drew most of our inspiration for the API from S
ELS: latency based load balancer, part 1
Load Balancing Most Spotify clients connect to our back-end via accesspoint which forwards client requests to other servers. In the picture below, the accesspoint has a choice of sending each metadataproxy request to one of 4 metadataproxy machines on behalf of the end user. Load balancer with 4 clients The client should get a quick reply from our servers, so if one machine becomes too slow, it sh
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Why We Switched Our Data Orchestration Service - Spotify Engineering
Product Lessons from ML Home: Spotify’s One-Stop Shop for Machine Learning - Spotify Engineering
Changing the Wheels on a Moving Bus — Spotify's Event Delivery Migration - Spotify Engineering
