Text clusters: Fighting spam content using neural networks and unsupervised ML – Part 2
February 16, 2022 9:46 amIn the part 1 of this blog series, we discussed the data science behind our new text clustering feature, as […]
Text clusters – Fighting spam using neural networks and unsupervised ML – Part 1
February 14, 2022 1:04 pmThe spam problem “Spam will be a thing of the past in two years’ time” – Bill Gates in 2004. […]
Seattle Office Holds First Public Event: Custom Machine Learning Models at Scale
April 21, 2017 6:01 pmOn April 19, 2017 the newly-opened Seattle Sift Science Research and Development Office hosted its first public speaking event: Alex Paino, […]
![Building Tools to Easily Run Experiments](https://engineering.sift.com/wp-content/uploads/2017/01/building-tools-to-run-experiments-700x580.jpg)
ML Experiments at Sift Science, Pt. 3: Building the Right Tools
January 9, 2017 8:51 amIn the first two posts of this three-part series we discussed how we ensure correctness when conducting and analyzing machine […]
![analyzing experiments](https://engineering.sift.com/wp-content/uploads/2016/12/comparing-experiments-700x580.jpg)
ML Experiments at Sift Science, Pt. 2: Analyzing Thousands of Models
December 21, 2016 9:14 amIn the first post on ML Experiments at Sift Science, we described how we minimize bias in our offline experimentation […]
![ML Experiments at Sift Science, Pt. 1: Minimizing Bias](https://engineering.sift.com/wp-content/uploads/2016/11/ML-Experimentation-at-Sift-700x580.jpg)
ML Experiments at Sift Science, Part 1: Minimizing Bias
December 1, 2016 2:44 pmAt Sift Science we use machine learning to prevent various forms of abuse on the internet. To do this, we […]