The Sass Mixins/Placeholders I Can’t Live Without for Responsive Web Design

July 24, 2015 4:50 pm Published by

Here at Sift Science, we just completed another big step in our ongoing marketing site redesign, overhauling the homepage and replacing old landing pages with [prettier, responsive, and more performant ones][1]. While the big performance improvements aren't quite ready to showcase yet (check in soon for more on that), I realized that there are a few custom Sass mixins and placeholders that I rely on heavily for responsive development—I'm not actually sure what I'd do without them—and I thought I'd share them here along with some CodePens so that other people might also take advantage of them!

Decision Forests: Taking Our Machine Learning to the Next Level

July 9, 2015 5:13 pm Published by

We're adding random decision forests to our machine learning solution, so get ready for an 18% improvement in Sift Score accuracy!This week, we launched an entirely new machine learning model called random decision forests, which will work alongside our existing models. Why? For an additional layer of prediction power, of course. With Sift Science’s decision forests in place, we expect that, on average, our customers will see a significant increase in fraud detection accuracy. This added model makes our online and large-scale learning capabilities even more robust! 

Turn Up The Bayes, Part 1

July 1, 2015 11:46 pm Published by

This week, we hosted the first session of our new summer speaking series (Turn Up The Bayes). I gave a talk on how we leverage a distributed database, HBase, to power an infrastructure that enables performant, distributed online learning. The following is a brief summary...but first, a quick introduction.Fraudsters always search for new ways to exploit opportunities at the expense of companies that provide legitimate goods and services. At Sift Science, we use real-time supervised machine learning to sabotage fraudster plots. As it turns out, the “real-time” portion of our product brings significant infrastructure challenges.