Sift’s 2021 Hackathon brings the entire company together for innovationOctober 20, 2021 5:49 pm
One of our values at Sift is “Ever Better”, meaning that we don’t settle for the status quo, and as […]
Engineering Management – Effective Design ReviewsMay 31, 2018 2:34 pm
Engineering Management Series The past few years have been a time of incredible growth at Sift and there is no […]
The rapidly evolving device and browser landscape allows us to collect increasingly rich data via our snippet. Because we host the snippet and our customers fetch it from us dynamically, expanding its functionality requires a strict eye on compatibility for all of our customers' end users. Our primary concerns are safety and iteration speed, and we’ve invested heavily in robust testing and deployment infrastructure to allow us to confidently roll changes out without spending weeks in manual testing.
Does Your Model Launch Have a SafetyNet?April 4, 2018 9:29 am
In the adversarial and constantly changing world of fraud detection and trust enablement, keeping up with the most recent online […]
How Sift Trains Thousands of Models using Apache AirflowMarch 20, 2018 12:25 pm
At Sift Science, engineers train large machine learning models for thousands of customers. We need processes and tools to do […]
2017 Sift Engineering in ReviewDecember 31, 2017 4:00 pm
2017 has been a pivotal year for Sift Science and the engineering team. We’ve delivered on amazing product launches, technological […]
Models in Disguise: How Sift Science Ships Non-Disruptive Model ChangesSeptember 12, 2017 10:10 am
TL;DR: We can transform the score distributions of new models to match those of old models, while preserving the new […]
Seattle Office Holds First Public Event: Custom Machine Learning Models at ScaleApril 21, 2017 6:01 pm
On April 19, 2017 the newly-opened Seattle Sift Science Research and Development Office hosted its first public speaking event: Alex Paino, […]
Browser DGAF (that you use React)March 16, 2016 5:58 pm
Adventures in React Performance DebuggingRecently I read Benchling’s 2-part series in debugging performance issues in React, and it really echoed the issues and solutions that I’ve been working through on the Sift Science Console. So I was inspired to chime in with some of my own React performance debugging experiences in what may become a short series itself.
Best practices for building large React applicationsMay 7, 2015 5:41 pm
Sift Science has been using React in production for almost a year now. In that time, we grew our application from a Backbone + React frankenstein hybrid app into one fairly large hierarchy of React components. In this post, we’ll describe some techniques and best practices that have helped us scale our UI code base with minimal friction. We’ll also walk through some common component design patterns.Hopefully this post will save you time and sanity and provide you with some new tools for maintaining a React code base that builds on itself (instead of breaking down) as the complexity of the UI grows.