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.

In this blog post, we share how we improved account recommendations with a model-based candidate generation framework.

In this blog, we’ll share how we use machine learning techniques to ensure people see the right amount of relevant and timely notifications.

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

In this blog, we describe how we separate Twitter’s pacing system from the serving stack to an independent service.

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.

This blog post shares optimization findings to speed up Transformer-based models’ CPU inference and improve computational demand in Google Cloud.

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.

In this blog, we share how we’ve accelerated efforts to increase the use of security keys to prevent phishing attacks at Twitter.

How we process large scale data in real time at Twitter while reducing latency and improving accuracy.