Pinterest is a visual feed of ideas, products, clothing, and recipes. Millions of users browse Pinterest to find images and text that are tailored to their interests.

Like most companies, Pinterest started with a large monolithic application that served all requests. As Pinterest’s engineering resources expanded, some of the architecture was broken up into microservices and Dockerized, which make the system easier to reason about.

To serve users with better feeds, Pinterest built a machine learning pipeline using Kafka, Spark, and Presto. User events are generated from the frontend, logged onto Kafka, and aggregated to build machine learning models. These models are deployed into Docker containers much like the production microservices.

Kinnary Jangla is a senior software engineer at Pinterest, and she joins the show to talk about her experiences at the company–breaking up the monolith, architecting a machine learning pipeline, and deploying those models into production.

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