Every day, hundreds of millions of new Tweets containing over 40 languages of
ever-shifting vernacular flow through Twitter. Models that attempt to extract
insight from this firehose of information must face the torrential covariate
shift that is endemic to the Twitter platform. While regularly-retrained
algorithms can maintain performance in the face of this shift, fixed model
features that fail to represent new trends and tokens can quickly become stale,
resulting in performance degradation. To mitigate this problem we employ
learned features, or embedding models, that can efficiently represent the most
relevant aspects of a data distribution. Sharing these embedding models across
teams can also reduce redundancy and multiplicatively increase cross-team
modeling productivity. In this paper, we detail the commoditized tools,
algorithms and pipelines that we have developed and are developing at Twitter
to regularly generate high quality, up-to-date embeddings and share them
broadly across the company.

本文介绍了 Twitter 在处理海量信息时的一种解决方案，通过采用嵌入模型，实现了同时考虑模型效率与数据分布的算法更新，从而降低因语言变化带来的协变量漂移、减少冗余，并极大地提高了团队建模的效率。