By Rumman Chowdhury and Aaron Gonzales on
We’re investing in privacy-enhancing technologies (PET) to pioneer new methods of public accountability and access to data in a manner that respects and protects the privacy of people on Twitter.
By Jing Ping Wong on
In this blog post, we share how we improved account recommendations with a model-based candidate generation framework.
By Prakhar Biyani and Stephen Ragain on
In this blog, we’ll share how we use machine learning techniques to ensure people see the right amount of relevant and timely notifications.
By Xiao Zhu and Mariko Wakabayashi on
Twitter Notebook is Twitter’s internal notebook solution. It provides a first-class development environment to Data Scientist and Machine Learning practitioners at Twitter. This blog shares features o
By Eddie Xie and Yuanjun Yang on
In this blog, we describe how we separate Twitter’s pacing system from the serving stack to an independent service.
By Michael Bronstein on
In this post, we show how tools from the fields of differential geometry and algebraic topology can be used to reinterpret GNNs and address some of their common plights in a principled way.
By Mariko Wakabayashi on
This blog post shares optimization findings to speed up Transformer-based models’ CPU inference and improve computational demand in Google Cloud.
By Chunxu Tang on
How we apply machine learning techniques at Twitter to forecast SQL query resource utilization during the development and maintenance of our large-scale SQL system.
By Nick Fohs and Nupur Gholap on
In this blog, we share how we’ve accelerated efforts to increase the use of security keys to prevent phishing attacks at Twitter.
By Lu Zhang and Chukwudiuto Malife on
How we process large scale data in real time at Twitter while reducing latency and improving accuracy.