Learn how we design a system that stores and retrieves ad impression metadata accurately and consistently in Twitter's ads ecosystem.

Introducing Twitter Text Editor, a standalone, flexible API that provides a full-featured rich text editor for iOS applications.

A new neural network architecture for dynamic graphs

Kafka is traditionally used to power streaming infrastructures. Learn how we used Kafka as a storage system to build the Account Activity API Replay Feature.

Twitter is introducing a visual quality assessment method that relies on computing VMAF percentiles. Compared to existing techniques, the method is effective and intelligible to non-video engineers.

Learn how our Revenue Platform team fortified Twitter's real time ad spend architecture to prevent overspend.

In this blog post we describe the dataset that Twitter released for the RecSys 2020 Challenge and the insights we had from the winning teams.

Using our customized data and model parallel distributed training strategy provides training speed improvements of up to 60x over single-node training for sparse machine learning models at Twitter.

We use a combination of data parallelism and model parallelism in a customized distributed training strategy to enable fast training of large sparse machine learning models at Twitter.

We detail the optimizations behind our custom approach to distributed training. We begin with the distribution strategies provided by TensorFlow, and the difficulties we had using them at Twitter.